Research ArticleAdopting De Novo Programming Approach on IC DesignService Firms Resources Integration

James K C Chen

Department of Business Administration Asia University No 500 Lioufeng Road WufengTaichung 41354 Taiwan

Correspondence should be addressed to James K C Chen kcchenasiaedutw

Received 28 September 2013 Revised 23 December 2013 Accepted 23 December 2013 Published 18 February 2014

Academic Editor Ching-Ter Chang

Copyright copy 2014 James K C Chen This is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

The semiconductor industry has very important position in computer industry ICT field and new electronic technologydeveloping The IC design service is one of key factor of semiconductor industry development There are more than 365 IC designservice firms have been established around Hsinchu Science Park in Taiwan Building an efficient planning model for IC designservice firm resources integrating is very interest issue This study aims to construct a planning model for IC design service firmimplementation resources integration This study uses the De Novo programming as an approach of criteria alternative to achieveoptimal resource allocation on IC design firm Results show the IC design service firm should conduct open innovation conceptand utilizes design outsourcing obtains cost down and enhance IC design service business performance This plan model of DeNovo programming is not only for IC design service firm and also can apply to the other industrial implementation strategicallianceintegrating resource This plan model is a universal model for the others industries field

1 Introduction

The semiconductor industry has very important position incomputer industry information communication technology(ICT) field and new electronic technology developing Theintegrate circuit (IC) design is one of the key factors thatinfluences semiconductor industry development to succeedor fail Taiwanrsquos semiconductor industry is successful case andcomplete supply chain system of IC industry The IC designservice is one of IC semiconductor industries that is a newbusiness model of semiconductor manufacture process Thesemiconductorrsquos maker design IC by internal resource of firmin the past Comparative IC design with internal resourceof firm is low efficiency and high cost than through designoutsourcing Outsourcing therefore is a crucial strategic deci-sion formany organizational functions namely managementof human resource accounting management of informa-tion systems and management of supply chain [1] A highperformance supply chain system offers the right producthigh quantity delivery in right place at right time and

reasonable price Optimal production and design planningpolicies develop a centralized supply chain system under fullinformation sharing at an e-business model [2] Previousscholars have proposed many kinds of analytical approachas aids in conflict management of resource redistribution[3] Among the numerous approaches available for conflictmanagement multicriteria decision making (MCDM) is oneof the most useful methodologies MCDM is a dynamicsituation process methodology that includs managerial leveloperation level engineering level and business level [4]

Taiwanrsquos semiconductor industry is a globalization indus-try that offers the DRAM TQFP IC SOP TSOP and IC chipon global IC market The IC semiconductor industry supplychain is a complete and strong infrastructure in TaiwanThe semiconductors industry includes IC design IDM Faberfoundry assembly and test of IC Specially the IC designfirm is a flexible business model which scale permit fromone to hundreds of employees company Therefore how tointegrate these IC design service firmrsquos resources became avery important issue for IC semiconductors industries This

Hindawi Publishing CorporationMathematical Problems in EngineeringVolume 2014 Article ID 903056 13 pageshttpdxdoiorg1011552014903056

2 Mathematical Problems in Engineering

study aims to IC design service firmrsquos resources integrationstrategic alliance and resources allocation We are utilizingthe IC design service firm for empirical case study to examinethe result of integrated resources This paper introducesthe De Novo programming in aid of criteria alternative forstrategic allianceresource allocation on IC design servicefirm [5 6]

The issue of research innovation has attracted scholarattention and interest in the last century Innovation varies interms of products services processes and business practices[7] Open innovationmeans that valuable ideasresources cancome internally or externally from company Chesbrough [8]points out ldquoopen innovationrdquo is a paradigm that assumes firmscan and should use external ideas as well as internal ideaand through internal or external paths output the outcomeof innovation technology to market The firms can throughauthorization offer intellectual property (IP) to partner fornew technology and also can spin-off new organization tomarket running new business This study will be based onopen innovation concept exploring the IC design servicefirm and how to integrate IC design industry resourcesand make optimal resources allocation in IC design servicefirm This model of De Novo programming is not onlyfor IC design service industry but also can apply to theother industrial implementation strategic allianceintegratingresource allocation

The remainder of this paper is organized as followsSection 2 discusses related theories of integrating resourceplanning model Section 3 details the construction of anefficient planning model for integrating resource Section 4utilizes De Novo programming approach illustrating anempirical case to demonstrate how the proposed integratingresource model for IC design service firm Section 5 presentsconclusions implications and recommendations

2 Theoretical Background

This section explores the related theoretical background ofintegrating resource planning model in the literature

21 Integrating Resource with Project Management Previousstudies have often explored integrating resource using theresource-based view (RBV) of firm organization [9 10]Holcomb and Hitt [11] describe tangible or intangible assetsowned or controlled by firms as resources and organiza-tional routines that allow firms to effectively integrate anduse resources allocation to implement on their strategiesplanning Resources have two key features (1) they mustenable the creation of organizationrsquos value and (2) thisresources provide unique support that can resist duplicativeefforts from the competitors [9] A firmrsquos competitiveness oroperational performance always is dependent on their coreresources it possesses [12]

Resource-based view of integration originated from thetheory of the growth of the firm [13] Barney [9] puts forwardamore contemporary interpretation of RBV emphasizing theimportance of resources in guiding firm activity transactioncost and managing a firmrsquos portfolio of capabilities as it

is central to enhancing competitive advantage [9 14] Thedefinition of RBV theory describes the resources as eithertangible or intangible and as both heterogeneous and imper-fectly mobile among firms [10] RBV theory has been studiedextensively in business strategy in the past decade and hasbecome a popular explanation of performance heterogeneityat the firm level [15 16] According to RBV theory firms seekcomplementary resource allocation to create synergies andacquire sustainable competitive advantages [17] To respondquickly to a dynamic environment firms should considerconstructing and extending limited resources to develop acapability for sustainable competitive advantage [18]

The pharmaceutical industry utilizes project manage-ment aspects of scheduling arranged and resource allocationon RampD pipelines that is a practical approach to improv-ing management performance [19] Project planning andscheduling arranged has become an important managementtool for todayrsquos complex investment environment [20 21]Project management can enhance resource allocation plan-ning quality and help organizations upgrade their perfor-mance [22] An efficient planning model that can aid firmsin achieving optimal resource allocation and consequentlythe best resource allocation outcome is not only desirable butnecessary for any organizationThrough projectmanagementconduct an effectively integrating resource planning modelfor IC design service firm of IC semiconductor industry isexpected

22 Through Alliance Planning Conduct Optimal ResourceAllocation Model Integrating IC design service humanresource into semiconductor industry has become an impor-tant issue in IC semiconductor industryMoore and Benbasat[23] suggest developing an instrument to help firms enhancetheir adoption of information technology innovation Theyregard firms as resource bundles for strategic alliance capa-bilities and competencies that provide a distinct sourceof competitive heterogeneity [24] Faced with insufficientresources firm should seek for more internal resourcesand leverage external resources that support firmrsquos researchdevelopment and can reallocate resources according to needsand goals of organization An efficient planningmodel wouldenhance the utilization rate of resources and improve RampDperformance Through alliance planning integrate internaland external resources of IC semiconductor industry that canenhance business operation performance

Competitive strategy is an important approach of man-agement issue Organizations considered competitive strat-egy very useful for resource planning and increasing effi-ciency across organizations management [25] Both compar-ative advantages in resources allocation and resource deploy-ment contribute to enhanced performance [26] To achievehigh performance of operational the top managers shouldprovide a conspicuous goal of strategic alliance direction [27]Strategic alliance planning is an approach of managementin defining a companyrsquos future progression direction anddeveloping a plan for its development in the future [28ndash31] Integrating IC design human resources into IC designservice through strategic alliances approach that can helpfirms improve business performance The IC design service

Mathematical Problems in Engineering 3

firm also can utilize strategy alliance planning to create muchmore added value on business operation

23 De Novo Programming Approach for Resource AllocationThe classical De Novo programming method proposed byZeleny [5 32 33] is an effective approach in dealing withoptimal design problems Currently system analysis anddesign have become important managerial and operationalissues in many countries and regions of water resources [34]Theoriginal idea ofDeNovo programming does not advocateproduction of individual or separate resources Speciallythe human resources are related and not independent ofIC design firms In the real world it is virtually impossibleto optimize all criteria when confronting a situation Theconcept of trade-off then becomes useful when consideringmultiple criteria andwhen operating under limited resourcesZeleny [35] regards trade-offs as properties of an inadequatelydesigned system and can thus be eliminated through design-ing a better preferably optimal system The De Novo pro-gramming approach can deal with an optimization problemsolution with multiple criteria Hence this approach wasadopted in this study to construct the integrating resourcesplanning model that can be universally applied to the othercase for different industries

24 Open Innovation Theory Open innovation has emergedas a new key theory in recent years It is a novel model fororganizing technological innovation in large RampD intensivefirms [36] According to open innovation model firmscan and should use both internal and external ideas andaccess the markets through both internal and external pathswhen they advance new technology [8] Open innovationoffers systematic incentives and explores a wide range ofinternal and external sources for innovative opportunitiesconsciously integrating such exploration with firmrsquos capabili-ties and resources and broadly exploiting those opportunitiesthroughmultiple channels [37] Open innovation has becomeincreasingly important for both practice and theory Organi-zations need shorter innovation cycles time and lower RampDcosts

The managerial challenges of open innovation involveutilizing external knowledge then identifying useful externalknowledge and integrating that knowledge into firm Forexample new products have significant trade-offs betweeninnovation speed in new product development costs andcompetitive advantage in relying on external knowledgerather than benefit in current internal knowledge [38] Theopen innovation phenomenon is reinforced by increasingglobalization of research technologies and innovation newinformation and communication technologies and poten-tials of new organizational forms and business models [39]

Chesbrough et al [36] show that the open innovationparadigm treats RampD as an open system in which valuableideas come from inside or outside of company and go tomar-ket from inside or spin-off from the firm (see Figure 1) Thisstudy combines the concepts of open innovation strategicalliance andDeNovo programming approach that integratesIC design service firmrsquos resource into IC semiconductorsindustry

3 Methodology

As seen in the above discussion integrated resources playsa central role in universal industry Specially in IC designservice firmrsquos allocates resource and promotes sustainabilityefforts Next section explores the construction of an efficientplanningmodel for strategy alliance alternative to integratingresources using De Novo programming

31 Integrating Resource Allocation The semiconductorindustry face too many challenge including cost downcompetitor treat human resource constraint and otherresources limitation which in turn affects IC designeffectiveness and business operational performance Optimalhuman resource allocation bymathematic programming thusbecomes a key issue in IC design service firms Mathematicprogramming distributes limited resources to competingactivities to achieve optimal resource allocation Linearprogramming is the most popular mathematic programmingapproach Kantorovich and Koopmans [40] develop linearprogramming formula which can be described as follows

Max Cx

st Ax le b

x ge 0

(1)

Here C = C119902times119899 and A = A119898times119899 are matrices b = (1198871

119887119898)119879isin 119877119898 and x = (119909119894 119909119895 119909119899)

119879isin 119877119899 Let the 119896th

row of119862 be denoted by119862119896 = (119888119896119894 119888119896

119895 119888119896119899) isin 119877119899 and119862119896119909

(119896 = 1 119902) is the 119896th evaluation criterion or alternativeobjective function This linear programming problem canbe solved in several ways which include using the simplexmethod or the interior-point algorithm that allocates lim-ited resource problems Although mathematic programmingoffers a solution method to the resource allocation problemsit is unreasonable to assume additivity when extending thismethod to manage an alliance resourceThis situation is baseon additivity which presumes that all productive elementsare independent and the total effects equal the summationof each individual effect Such assumption does not fit firmrsquosneeds when the firm expects to create a synergies effect ofbusiness operations

The concept of emerging mass customization has beenproposed for solving the problem of element independenceGenerally a company can obtain profits in twoways One is toincrease revenue with higher unit price through customizingand the other is to reduce the cost of unit through max eco-nomic scale of production With element independence it isimpossible to reduce cost of unit and increase revenue simul-taneously Utilizing the concept of mass customization canrelease limited element independence This study assumesthat there exists a market alliance between companies 119860and 119861 with 120587119860 and 120587119861 denoting their respective profits Thegoal of a company is to maximize profits and the feasiblesolutions are within the space surrounded by dotted linesshown in Figure 2 Compromise solutions are typically thebest decision in traditional mathematic programming and

4 Mathematical Problems in Engineering

Licensing

Technology spinoffsInternal

technology

base

External

technology

base

Other firmrsquos

New

Current

R D

Technology insourcing

market

market

market

Figure 1 The open innovation paradigm (sources from [36])

0

Max

Max

Feasible space

120587A A C

B

120587B

(a)

A C

B

(b)

Figure 2 Feasible options obtained using linear programming

they fall into 119860 cap 119861 Options space contained in points 119860119861 and 119862 that include the ideal point 119862 they are unavailableoptions space what caused with utilizing linear programmingadditivity

According to the assumption of additivity combiningalliance resources allows not only 1 + 1 = 2 but also can obtain1 + 1 gt 2 Therefore synergies concept are popular reasonsfor obtaining optima result through resource alliances [41] Inother words traditionalmathematic programming is rationaland available when a firm has resource constraints thatcannot change if it is produced individually [42] Howeverthe traditionalmethod is no longer suitable when redesigningsystems and the optimal solution was adopted throughstrategic alliances with difference firms

This research uses De Novo programming to releaselimited element independence and to solve the problem ofan optimal resource allocation portfolio through resourcealliances to achieve the aspiration goal

32 De Novo Programming for Alliances Approach The DeNovo programming is one of MCDM method that canremodify the systems to achieve an aspiration goal of firmrsquosexpectation By releasing various constraints the De Novoprogramming attempts to break limitations to achieve theoptimal solution Through formation strategic alliance andresource integration the current work extends the De Novoprogramming to obtain an optimal solution [43ndash45] TheDe Novo perspective combines transaction cost theory andresource-based view (RBV) to provide a holistic perspectivefor achieving an aspiration level [46] In this researchfirm seeks strategic allianceresource integration accordingto firmrsquos needs If the minimum alliance cost lies betweendesign cost and human resource cost Semiconductorrsquos firmshould seek strategic allianceresource integrated for ICdesign human resources sharing The IC design service costcan be modified as if allianceintegration cost is less thanindividual firmrsquos cost summary (le sum119873

119894=1)

Mathematical Problems in Engineering 5

The firm should seek allianceintegration with the otherpartner

From the RBV view firms seek resource alliances capa-bilities by allying with a partner to create synergies based onRBV theory The rule of the RBV can also be modified asif allianceintegration benefit is larger than individual firmrsquosprofit summary (ge sum119873

119894=1)

The firm should seek allianceintegration with the otherfirms

Now we involve the cost and RBV theory into the DeNovo programming if the firm alternatives are based on twodifferent resources 119878 and 119879 the rule of resource integratingcan be expressed as follows

if 119864 (119878 cup 119879) minus 119880 (119862119878119879) gt 119864 (119878) + 119864 (119879) minus 119880 (119862119878) minus 119880 (119862119879) (2)

then the firm should seek resource integration with partnerof industry cluster

Also it can express a general formula as follows

if 119864 (1198781 cup 1198782 sdot sdot sdot cup 119878119873) minus 119880 (119862 alliance cost )

ge

119873

sum119894=1

[119864 (119878119894) minus 119880 (119878119894)] 119894 = 1 2 119873(3)

where119864(sdot) is the benefit function119880(sdot) is the cost function119862119878and 119862119879 denote the total design cost in 119878 and 119879 respectivelyand 119862119878119879 denotes the allianceintegration cost between 119878

and 119879 The probability of 119878 and 119879 events seeking strategicallianceresource integration can be demoted respectively as

119901 (119904) = 1 120582119864 (119878 cup 119879) minus 120579119880 (119862119878119879) gt 119864 (119878) minus 119880 (119862119878)

0 120582119864 (119878 cup 119879) minus 120579119880 (119862119878119879) lt 119864 (119878) minus 119880 (119862119878)

(4a)

119901 (119879) =

1 (1 minus 120582) 119864 (119878 cup 119879) minus (1 minus 120579)119880 (119862119878119879)

gt 119864 (119879) minus 119880 (119862119879)

0 (1 minus 120582) 119864 (119878 cup 119879) minus (1 minus 120579)119880 (119862119878119879)

lt 119864 (119879) minus 119880 (119862119879)

(4b)

where 120582 denotes the percentage of increasing alliance benefitin 119878 and 120579 denotes the percentage of reducing alliance costin 119878 Now we can involve De Novo programming into theplanning model of resource integrating that can be expressedas

max 119864 (119878 cup 119879) minus 119880 (119862119878119879)

st 119908119909 le 119861 119909 ge 0(5)

where 119908 = 119901119860 = (1199081 119908119899) isin 119877119899 and 119901 = (1199011 119901119898) isin

119877119898 and 119861 isin 119877 present the unit price of resources and the totalavailable budget Then the knapsack solution is

119909lowast= [0

119861

119862119896 0]

119879

(6)

where

119888119896

119888lowast119896

= max119895

(119888119895

119888lowast119895

) (7)

Max 0

Max

Feasiblespace

120587R

120587AA

C

B

Figure 3 Feasible options obtained using De Novo programming

The optimal solution to (5) is given by (6) and

119887lowast= 119860119909lowast (8)

The final allianceintegration benefit (Ψ(119878lowast)) in 119878 is

Ψ (119878lowast) = 1198941015840119887lowast119880(minus119862119878119879) (9)

where 119894 is the identity column vector According to (9) wecan judge whether or not the firm should seek strategicallianceresource integration by (3) (4a) and (4b) Further-more using De Novo programming we can easily achieveoptimal resource allocation and create synergies betweenalliances The difference between traditional mathematicprogramming andDeNovo programming lies in the ability oftheDeNovoprogramming to redefine its boundaries throughsystem redesign reconfiguration or reshaping [33] Figure 3shows the difference in feasible options space obtainedthrough De Novo programming method

The greatest difference between Figures 2 and 3 is thatthe unavailable solutions before were made available throughDe Novo programming now In other words the idealpoint 119862 is the goal of optimal solution through strategicallianceresource integrating approach

4 Empirical Study Practice

It is commonly believed that integrating IC design humanresources into IC semiconductor supply chain system canenhance operation performance and such integration wouldrequire a complete planning model to ensure effectivenessThe proposed planning model is applied to IC design servicefirms in Taiwan as a empirical case study to examine theplanning model feasibility

41 The Case Study of Taiwanrsquos IC Service Design FirmTaiwanrsquos semiconductors industry is a globalization businessspecializing as the worldrsquos number one of DRAM IC foundrymanufacturer The IC semiconductors industry is a completesupply chain that includes IC design service supplier andcustomer The IC supply chain system developed becamefirms cluster with difference supply chain speciality needs

6 Mathematical Problems in Engineering

IC design service

Supplier Customer

Servicecluster

Suppliercluster

Customercluster

Figure 4 The supply chain of IC semiconductor industry

(see Figure 4) The IC design service is one of semiconductormanufacturing processing before launching into IC foundryThe IC design service firms obtain the order from customerand through project management controls the design sched-ule for customer needs More than 365 IC design servicefirms have been established around Hsinchu Science Parkin Taiwan This IC service design firm cluster situation isvery special in the world Consequently studying Taiwanrsquos ICdesign service firms is a very interesting empirical case

42 The Problems of IC Semiconductor Industry CurrentlyTaiwanrsquos semiconductors face a very slow economics situationof business operation Government release encourage policytry to save the semiconductor industry but these policies wasfail The economics of semiconductor continues going downof economic curve The IC industry external environmentalfactors are multiples and dynamics specializing on theIC design service sector Also the IC design service firmrequires one high performance project management to helpmanage related task Integrating internal human resource andleveraging external resource and adjusting business strategyformatch customer needs of IC designmarket As such firmsshould offer just-in-time mechanism of services for productsdelivery and quick response system for customers A little losson service process could bring the result of lose customerTherefore IC design firm should determine to operate atlowest cost and high quality level for IC design service Highperformance IC design service systems should be consideredin the semiconductor supply chain system

43 IC Design Service Firms The IC design skill is one of keytechnical of IC semiconductor manufacturing system It is

a new business model emerging as a vertical disintegrationproduct in the IC semiconductor industry The IC semicon-ductor combines multiprocesses into one complete systemincluding IC design and an IDM (integrated device manu-facturer) fabrication assembly test in the 1970s IC-designsand IDM were separated in the 1980s from one complete setsystem to develop two subsystems of IC design and IDMfabrication and continues developing including IC designand IDM fabrication and assembly The IC semiconductorindustry after the 1980s modified processing to include thesystem IC design IDM fabrication IC foundry IC assemblyand IC test on the subsystem The IC design subsystem after2001 extends from one to three parts including SiP (systemin package) design system and IC design The IC designservice firm has become an important process of the ICsemiconductors industry Figure 5 shows IC design servicedevelopment trajectory from 1960 to 2013

The IC design service firm is a high technology knowl-edge industry The IC design service firm requires pro-fessional human design who must have engineering back-ground specialized design technology knowledge and alsoneed high speed internet networks a convenient communi-cation digital platform and a high operation performanceThis study expects a building of an MCDM model with DeNovo programming for integrating the resource of IC designservice firmsThe IC design service firm is multidimensionaland highly competitive focusing on special professionaltechniques and quick response of design services De Novoprogramming helps to plan service model for achievingaspiration levels of the IC design service firms

44 The Relationship of IC Design Service Supplier andCustomer The IC semiconductors industry is a multipleproduction process that includes design fabrication foundrytesting assembly and delivery of products to customer TheIC design service is a substructure under the IC supplierproviding IC design service for supplier or customer The ICdesign service firms offer system in package (SiP) design todirect customer or IC design and IC layout design for supplier(see Figure 6) The IC design service firms offers value addedat each design service process for example highlightingperformance powerful function low production cost systemon chip (SoC) and redownsizing IC design space Figure 6shows relationship between of IC design service supplier andcustomer

The IC design service firms not only provides designservice for IC semiconductors manufacturing systems butalso offers new innovative ideasfunction on IC chip forcustomer The IC design service firms generally providesprofessional IC design knowledge and IC foundry techniquesthat can obtain greater economics efficiency of semiconduc-tors production the result should be increased IC design ser-vice business opportunity Consequently adopting innovativeideas becomes a very important task in IC design serviceprocessing

45 Through De Novo Programming Achieving AspirationGoal This study expects through De Novo programming to

Mathematical Problems in Engineering 7

After 20001980s to 1990sBefore 1970s

IC design

System IC-ASSP IC-ASIC SOC-IP

SystemSystemSystemSystemSystemIC designIDM fabassembly

test

DS

IC design

IDM fab

Foundry

Assembly

Test

IC design

IDM fab

Foundry

Assembly

Test

IC design

IDM fab

Assembly

SystemIC designIDM fabassembly

test

20001990198019701960 2013

SIP

Figure 5 The IC design service development trajectory from 1960 to 2013 year

IC designservice

Supplier CustomerFinished products

SiP design service

Design requirement IC design service

Order requirement

IC layout order

Figure 6 The relationship of IC design service supplier andcustomer

achieve optimal resource integrating purpose The IC designservice firm must consider resource constrain under mul-tiple objectives alternatives before making decisions Firmface multiple objectives and evaluation criteria of problemnamely dealing with resources integrating and adjustingbusiness strategic to achieve the aspiration goal of businessIn general through trade-off skill it is almost impossible toobtain optimization of all criteria of a given system Zeleny[32] states the new system can eliminate the resource con-strain through redesign then preferably obtain optimizationresults what can improves theTrade-off problems Zeleny [35]proposes the optimal portfolio resource allocation conceptwhich designs a resource integrated system that is individualresource levels cannot determine separately so new designsystems do not have trade-offs to consider Zeleny [33]develops a De Novo programming method for designing an

Low

High

High

For achieving the aspireddesired

level

Good

The best

The best

Efficiencies

Perfo

rman

ce

Figure 7Through De Novo programming achieving the aspirationgoal

optimal system by reshaping the feasible set [33] Firm canmake strategy alliances with partner of supply chain systemand achieves resource integration adjusting the businessgoal remodifying the business model and justifying targetcustomer services This study expects through De Novo pro-gramming approach to compute the data of resource integrat-ing and justify the resource allocationobjective alternativefrom a good level to best level thus achieving aspiration goal(see Figure 7)

8 Mathematical Problems in Engineering

46 Empirical Case Study

461 A Case Study of IC Production Firm A general case ofIC production problem involving two IC products GPS andcell phone of IC in quantities 1199091 and 1199092 each consuming fivedifferent resources (unitmarket prices of resources are given)The data is summarized as shown in Table 1

The costs of the given resources portfolio

(28 times 32) + (35 times 30) + (10 times 55)

+ (18 times 10) + (10 times 28) = $2956(10)

Unit costs of producing one unit goods of the two products

1199091 = (28 lowast 6) + (35 lowast 3) + (10 lowast 14) + (18 lowast 2)

+ (10 lowast 6) = $509

1199092 = (28 lowast 2) + (35 lowast 5) + (10 lowast 6) + (18 lowast 5)

+ (10 lowast 8) = $461

(11)

Expected profit margins (price-cost) arethe profit of 1199091 product = $549 minus $509 = $40unitthe profit of 1199092 product = $491 minus $461 = $30unit

Maximizing total value of function 11989111198911 = 401199091 + 301199092 (12)

Maximizing total quality index 11989121198912 = 81199091 + 101199092 (13)

Maximizing levels of two products calculated by De Novoprogramming

max 1198911 = 401199091 + 301199092

max 1198912 = 81199091 + 101199092

st 61199091 + 21199092 le 32

31199091 + 51199092 le 30

141199091 + 61199092 le 55

21199091 + 51199092 le 10

61199091 + 81199092 le 28

1199091 1199092 ge 0

(14)

Maximum 1198911 in profit

max 1198911 997888rarr 1199091 = 371

1199092 = 052

119891lowast

1= 40 times 371 + 30 times 052 = $164

(15)

Maximum 1198912 in total quality index

max 1198912 997888rarr 1199091 = 371

1199092 = 052

119891lowast

2= 8 times 371 + 10 times 052 = $3488

(16)

Minimizing the total cost by considering the followingconstraints

min 5091199091 + 4611199092

st 401199091 + 301199092 le 164

81199091 + 101199092 le 3488

(17)

Maximum 1198911 in profit

max 1198911 997888rarr 1199091 = 371

1199092 = 052

119891lowast

1= 40 times 371 + 30 times 052 = $164

(18)

Maximum 1198912 in total quality index

max 1198912 997888rarr 1199091 = 371

1199092 = 052

119891lowast

2= 8 times 371 + 10 times 052 = $3488

(19)

Cost of the newly designed system

(28 times 2328) + (35 times 1368) + (10 times 5506)

+ (18 times 1002) + (10 times 2642) = $21258(20)

The new portfolio of resources proposed by the consultant isshown in Table 2

The result of data analysis shows that the new planningsystem cost is $21258

The given resource portfolio cost is $2956 Comparisonbetween original and new planning model we found outthat the new planning model can reduce cost to $8305 andthat new resource planning model is one reliable modelConsequently the IC design service firm can follow the newplanningmodel ofDe novo programming to process resourceintegration

462 The Case of IC Design Service Firm The IC designservice firm is a professional workshop that focuses on theIC design task dependent on what design they can provideservice The IC design service task is also a project-basedplanning under a large RampDproject with business developingstrategy A project-based firm uses external delivery projectsfor business purposes [47 48] This study utilizes a real caseof IC design project management to practice on IC designservice firm Project planning may include many subprojectsthat depend on different cases of customerrsquos demand Centralfeatures of project management were identified according toindividual project uniqueness human resources of projectand business network complexity discontinuity of demandand relationships between other projects and considerablefinancial commitment of the parties [49 50] Conductingaspiration project planning under resource constraint is atarget issue for the IC design service firm This real caseincludes five different resources which included humanpower resource design equipments design materials special

Mathematical Problems in Engineering 9

Table 1 The IC of GPS and cell phone production requiring material

Unit price ($) Material of production processTechnological coefficients

Resource portfolio(units)(Resource requirement)

1199091

1199092

28 Silver wire 6 2 3235 Golden wire 3 5 3010 Silicon Crystal 14 6 5518 Chemistry material 2 5 1010 Conductor material 6 8 28

Table 2 The new resource portfolio planning of IC of GPS and cell phone

Unit price ($) Material of production processTechnological coefficients

Resource portfolio(units)(Resource requirement)

1199091

1199092

28 Silver wire 6 2 232835 Golden wire 3 5 136810 Silicon Crystal 14 6 550618 Chemistry material 2 5 100210 Conductor material 6 8 2642

technical human and design outsourcing with two differentproject teams for the A company customer to performthe IC design task The current study names the twoprojects as the 1198751 and 1198752 project team programs This realplanning program case utilizes the De Novo programmingfor adjustable resource allocation and seeks out minimumcost with maximum performance This work transitions thefive kinds of resources (design equipments design humanpower design material special technical human and design-outsourcing) in the case study to dollar amounts for easy costcalculation Design equipments cost is $380 per unit designhuman power labor cost is $240 per unit design materialresource cost is $200 special technical human cost is $260per unit and design outsourcing cost is $120 per unit

Costs of given resources portfolio

(380 times 26) + (240 times 50) + (200 times 30) + (120 times 10)

+ (260 times 16) = $33240(21)

Unit costs for running each project of the two projects

1198751 = (380 lowast 8) + (240 lowast 10) + (200 lowast 4)

+ (260 lowast 6) = $7800

1198752 = (380 lowast 6) + (240 lowast 4) + (200 lowast 4)

+ (120 lowast 4) = $4520

(22)

Expected profit margins (price cost) are

1198751 = $8200 minus $7800 = $400unit

1198752 = $4820 minus $4520 = $300unit(23)

Maximizing total value of function 1198911

1198911 = 4001199011 + 3001199012 (24)

Maximizing total quality index 1198912

1198912 = 81199011 + 101199012 (25)

Maximizing levels of the two projects calculated by De Novoprogramming

max 1198911 = 4001199011 + 3001199012

max 1198912 = 81199011 + 101199012

st 81199011 + 61199012 le 26

101199011 + 41199012 le 50

41199011 + 41199012 le 30

41199012 le 10

61199011 le 16

1199011 1199012 ge 0

(26)

Maximum 1198911 in profit

max 1198911 997888rarr 1199011 = 267

1199012 = 078

119891lowast

1= 400 times 267 + 300 times 078 = $1300

(27)

10 Mathematical Problems in Engineering

Table 3 1198751and 119875

2project team program require resource of IC design service

Unit price ($) Resources of IC designProject coefficients

Resource portfolio(units)

(Resource requirement)1198751

1198752

380 Design equipments 8 6 26240 Human power resource 10 4 50200 Design material 4 4 30120 Design outsource 0 4 10260 Special technical human 6 0 16

Table 4 The new resource portfolio planning model of 1198751and 119875

2project team program

Unit price ($) Resources of IC designProject coefficients

Resource portfolio(units)(Resource requirement)

1198751

1198752

380 Design equipments 8 6 26240 Human power resource 10 4 325200 Design material 4 4 13120 Design outsource 0 4 0260 Special technical human 6 0 195

Maximum 1198912 in total quality index

max 1198912 997888rarr 1199011 = 267

1199012 = 078

119891lowast

2= 8 times 267 + 10 times 078 = $2916

(28)

Minimizing the total cost by considering the followingconstraints

min 78001199011 + 45201199012

st 1198911 = 4001199011 + 3001199012 le 1300

1198912 = 81199011 + 101199012 le 2916

(29)

Maximum 1198911 in profit

max 1198911 997888rarr 1199011 = 325

1199012 = 0

119891lowast

1= 400 times 325 = $1300

(30)

Maximum 1198912 in total quality index

max 1198912 997888rarr 1199011 = 325

1199012 = 0

119891lowast

2= 8 times 325 = $26

(31)

Cost of new design planning

(380 times 26) + (240 times 325) + (200 times 13) + (260 times 195)

= $25350(32)

New portfolio of resources proposed by the consultant isshown in Table 4

47 Discussions

471 The General Case of IC Production Firm The datashow the empirical case study using the De Novo program-ming which remodifies the planning model and adjusts theresources allocation portfolio The new plan model offersthe best optimized resource integration and reduces losingand enhances firmrsquos competitiveness Technical problemsprogramming are discussed as follows

(i) The original plan needs a total cost of $2956 withgiven resources portfolios

[(28 times 32) + (35 times 30) + (10 times 55) + (18 times 10)

+ (10 times 28) = $2956] (33)

(ii) The new planning model of resource allocation onlyrequires cost of $21258

[(28 times 2328) + (35 times 1368) + (10 times 5506)

+ (18 times 1002) + (10 times 2642) = $21258] (34)

(iii) The newly planning model can save $8302 costConsequently firm seeks new planning model tointegrate resource

$2956 minus $21258 = $8302 (35)

(iv) The newly planning model use the De Novo pro-gramming to reduce resource loss and get betterperformance than the original planning model Thispaper also utilizes the empirical case examination ofDe Novo programming for IC design service firmperformance The new planning model obtains muchbetter resource integration

Mathematical Problems in Engineering 11

472 The Case of IC Design Service Firm Firms have massproduction capacity limitation per one time period due tolimited resources especially for the preceding competitivefacility location problem on the decentralized supply chain[51] The IC design service firm is a standard project-basedprogrammodel Many project teams program through coop-eration under a large design program for different customerare needed This study utilizes a case study of IC design firmfor planning an IC design to customer serviceThis case studyincludes two project teams The 1198751 and 1198752 project with twodifferent plan programming on IC design see (Table 3) Oneproject team leader prefers hiring a special technical engineerfor the IC design task that can maintain the IC design keytechnician and train other staffThe other project team leaderprefers outsourcing design that reduces cost The IC designservice firm chooses the alternative optimal project teambased on the result of De Novo programming

The resource portfolio of firmrsquos original cost is as follows

(380 times 26) + (240 times 50) + (200 times 30) + (120 times 10)

+ (260 times 16) = $33240(36)

The cost of newly planning model through the De Novoprogramming is as follows

(380 times 26) + (240 times 325) + (200 times 13) + (260 times 195)

= $25350(37)

The new planning model saves cost as follows

$33240 minus $25350 = $7890 (38)

The IC design firm adopting1198751 project team cost is as follows

1199011 (8 times 26) + (10 times 325) + (4 times 13) + (6 times 195) = $702(39)

The IC design firm adopting1198752 project team cost is as follows

1199012 (6 times 26) + (4 times 325) + (4 times 13) = $338 (40)

The result of data analysis shows through the De Novoprogramming method the IC design service firm that canfind out which project team is the optimal solution In thisempirical case adopting 1198752 project team can save design costmuchmore than1198751 projectThe new planningmodel reduces$7890 in cost compared with the original planning modelSo the newly planning model is the best choice for achievingaspiration goal

5 Conclusion

The IC semiconductor industry faces uncertainty anddynamic external environment especially at IC design servicefirm that needs one resource integrating programmingmodelfor new product development [52] Adopting an efficientstrategic alliancesresource integrating planning model is akey issue for achieving firmrsquos business goals especially astheir resource was constrainedThe present study considers a

planningmodel based on resource constraintsThis study uti-lizes the De Novo programming for achieving firmrsquos strategicgoal The De Novo programming can help firms find out anoptimal resource integration base on firm financial considerThis planning model provided reliable systematize approachthat combines alliances from external resources and changethem to internal resources application The project team1198752 adopting open innovation concept through outsourcingreduces design cost and enhances design performance DeNovo programming not only obtains optimal resource inte-gration but also promotes the conceptual of strategic alliancesfor firmrsquos cooperation between each other

51 Management Implication This research aims to create aresource integrating plan model through IC design servicefirm as an empirical case for examining the result of theplanning model The planning model is based on openinnovation theory exploring the IC design service firmwhichintegrates IC design resources and makes optimal resourcesintegrate in IC design service firm The planning model ofDe Novo programming is not only for IC design service firmbut also can apply to the other industrial implementationstrategic allianceresource integration This planning modelis a universal model for the other industry fields

52 Suggestion The IC design service industry is a veryspecial industry We suggest the extend to the other industrythat can find out different results Suggestion utilizes the otherapproach of MCDMmethod exploring different case studiesof industry

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

The authors appreciate two blind reviewers who have pro-vided very excellent comments These comments are verygood for the authors to improve the quality of the paperAlso thanks to the editor Professor Chang for offering lots ofexcellent suggestions that helped enhance this paperrsquos quality

References

[1] R Varadarajan ldquoOutsourcing think more expansivelyrdquo Journalof Business Research vol 62 no 11 pp 1165ndash1172 2009

[2] G Forgionne and Z Guo ldquoInternal supply chain coordinationin the electric utility industryrdquo European Journal of OperationalResearch vol 196 no 2 pp 619ndash627 2009

[3] R George and R Kabir ldquoBusiness groups and profit redistribu-tion a boon or bane for firmsrdquo Journal of Business Research vol61 no 9 pp 1004ndash1014 2008

[4] L Duckstein and S Opricovic ldquoMultiobjective optimization inriver basin developmentrdquoWater Resources Research vol 16 no1 pp 14ndash20 1980

12 Mathematical Problems in Engineering

[5] M Zeleny ldquoA case study in multiple objective design DeNovo programmingrdquo inMultiple Criteria Analysis OperationalMethods vol 1 pp 37ndash52 1981

[6] M Zeleny ldquoOptimal system design with multiple criteria DeNovo programming approachrdquo Engineering Costs and Produc-tion Economics vol 10 no 1 pp 89ndash94 1986

[7] S Christophersona M Kitsonb and J Michiec ldquoInnovationnetworks and knowledge exchangerdquo Cambridge Journal ofRegions Economy and Society vol 1 no 2 pp 165ndash173 2008

[8] H W Chesbrough Open Innovation The New Imperativefor Creating and Profiting from Technology Harvard BusinessSchool Press Boston Mass USA 2003

[9] J B Barney ldquoFirm resources and sustained competitive advan-tagerdquo Advances in Strategic Management vol 17 pp 203ndash2272000

[10] B Wernerfelt ldquoA resource-based view of the firmrdquo StrategicManagement Journal vol 5 no 2 pp 171ndash180 1984

[11] T R Holcomb and M A Hitt ldquoToward a model of strategicoutsourcingrdquo Journal of Operations Management vol 25 no 2pp 464ndash481 2007

[12] D J Collis ldquoA resource-based analysis of global competitionthe case of the bearings industryrdquo Strategic Management Jour-nal vol 12 no 1 pp 49ndash68 1991

[13] E Penrose The Theory of the Growth of the Firm John Wileyand Sons New York NY USA 1959

[14] E Anderson andH Gatignon ldquoModes of foreign entry a trans-action cost analysis and propositionsrdquo Journal of InternationalBusiness Studies vol 17 no 3 pp 1ndash26 1986

[15] J Chimhanzi and R E Morgan ldquoExplanations from themarketinghuman resources dyad formarketing strategy imple-mentation effectiveness in service firmsrdquo Journal of BusinessResearch vol 58 no 6 pp 787ndash796 2005

[16] J Fahy ldquoA resource-based analysis of sustainable competitiveadvantage in a global environmentrdquo International BusinessReview vol 11 no 1 pp 57ndash78 2002

[17] M A Harrison R E Hoskisson and D Ireland ldquoSynergies andpost acquisition performance differences versus similarities inresource allocationsrdquo Journal of Management vol 17 no 1 pp173ndash190 1991

[18] D J Teece G Pisano and A Shuen ldquoDynamic capabilities andstrategic managementrdquo Strategic Management Journal vol 18no 7 pp 509ndash533 1997

[19] V A Varma J F Pekny G E Blau and G V Reklaitis ldquoAframework for addressing stochastic and combinatorial aspectsof scheduling and resource allocation in pharmaceutical RampDpipelinesrdquoComputers andChemical Engineering vol 32 no 4-5pp 1000ndash1015 2008

[20] R Kolisch ldquoEfficient priority rules for the resource-constrainedproject scheduling problemrdquo Journal of Operations Manage-ment vol 14 no 3 pp 179ndash192 1996

[21] R Kolisch and S Hartmann ldquoExperimental investigationof heuristics for resource-constrained project scheduling anupdaterdquo European Journal of Operational Research vol 174 no1 pp 23ndash37 2006

[22] M J Leiblein ldquoThe choice of organizational governance formand performance predictions from transaction cost resource-based and real options theoriesrdquo Journal of Management vol29 no 6 pp 937ndash961 2003

[23] G CMoore and I Benbasat ldquoDevelopment of an instrument tomeasure the perceptions of adopting an information technologyinnovationrdquo Information Systems Research vol 2 no 3 pp 192ndash222 1991

[24] R E Hoskisson M A Hitt W P Wan and D Yiu ldquoTheoryand research in strategic management swings of a pendulumrdquoJournal of Management vol 25 no 3 pp 417ndash456 1999

[25] C C Miller and L B Cardinal ldquoStrategic planning and firmperformance a synthesis of more than two decades of researchrdquoAcademy of Management Journal vol 37 no 6 pp 1649ndash16651994

[26] S D Hunt A General Theory of Competition Sage ThousandOaks Calif USA 2000

[27] S Hart and C Banbury ldquoHow strategy-making processes canmake a differencerdquo Strategic Management Journal vol 15 no 4pp 251ndash269 1994

[28] E Drakopoulos ldquoEnterprise network planning and designmethodology and applicationrdquo Computer Communications vol22 no 4 pp 340ndash352 1999

[29] V Kumar B Maheshwari and U Kumar ldquoERP systems imple-mentation best practices in Canadian government organiza-tionsrdquoGovernment InformationQuarterly vol 19 no 2 pp 147ndash172 2002

[30] W R Lewis ldquoStrategic planningrdquo Hospital Materiel Manage-ment Quarterly vol 10 no 4 pp 57ndash63 1989

[31] D Wainwright and T Waring ldquoThree domains for imple-menting integrated information systems redressing the bal-ance between technology strategic and organisational analysisrdquoInternational Journal of InformationManagement vol 24 no 4pp 329ndash346 2004

[32] M Zeleny ldquoOptimal system design with multiple criteria DeNovo programming approachrdquo Engineering Costs and Produc-tion Economics vol 10 no 1 pp 89ndash95 1986

[33] M Zeleny ldquoOptimal given system versus designing optimalsystem the De Novo programming approachrdquo InternationalJournal of General System vol 17 no 3 pp 295ndash307 1990

[34] Y M Zhang G H Huang and X D Zhang ldquoInexact de Novoprogramming for water resources systems planningrdquo EuropeanJournal ofOperational Research vol 199 no 2 pp 531ndash541 2009

[35] M Zeleny ldquoTrade-offs free management via De Novo pro-grammingrdquo International Journal Operations and QuantitativeManagement vol 1 no 1 pp 3ndash13 1995

[36] H Chesbrough W Anhaverbeke and J West Eds Openinnovation Researching a New Paradigm Oxford UniversityPress 2006

[37] J West and S Gallagher ldquoChallenges of open innovation theparadox of firm investment in open-source softwarerdquo R amp DManagement vol 36 no 3 pp 319ndash331 2006

[38] E H Kessler P E Bierly and S Gopalakrishnan ldquoInternalversus external learning in new product development effects onspeed costs and competitive advantagerdquo R amp D Managementvol 30 no 3 pp 213ndash223 2000

[39] O Gassmann and E Enkel ldquoTowards a theory of open innova-tion three core process archetypesrdquo in Proceedings of the RampDManagement Conference (RADMA rsquo04) Lisbon Portugal 2004

[40] L V Kantorovich and T C Koopmans Problems of Applicationof Optimization Methods in Industry Federation of SwedishIndustry 1976

[41] T K Das and B-S Teng ldquoA resource-based theory of strategicalliancesrdquo Journal of Management vol 26 no 1 pp 31ndash61 2000

[42] Z Babic and I Pavic ldquoMulticriterial production planning byDe Novo programming approachrdquo International Journal ofProduction Economics vol 43 no 1 pp 59ndash66 1996

[43] J K C Chen and G H Tzeng ldquoPerspective strategic allianceand resource allocation in supply chain systems through the De

Mathematical Problems in Engineering 13

Novo programming approachrdquo International Journal Sustain-able Strategic Management vol 1 no 3 pp 320ndash339 2009

[44] J-J Huang G-H Tzeng and C-S Ong ldquoMotivation andresource-allocation for strategic alliances through the De NovoperspectiverdquoMathematical and ComputerModelling vol 41 no6-7 pp 711ndash721 2005

[45] J-J Huang G-H Tzeng and C-S Ong ldquoChoosing bestalliance partners and allocating optimal alliance resources usingthe fuzzymulti-objective dummyprogrammingmodelrdquo Journalof the Operational Research Society vol 57 no 10 pp 1216ndash12232006

[46] R M Grant ldquoThe resource-based theory competitive advan-tage implications for strategy formulationrdquo California Manage-ment Review vol 33 no 3 pp 114ndash135 1991

[47] K A Artto and K Wikstrom ldquoWhat is project businessrdquoInternational Journal of Project Management vol 23 no 5 pp343ndash353 2005

[48] J Soderlund ldquoOn the broadening scope of the research onprojects a review and a model for analysisrdquo InternationalJournal of ProjectManagement vol 22 no 8 pp 655ndash667 2004

[49] B Cova P Ghauri and R Salle Project Marketing BeyondCompetitive Bidding JohnWiley amp Sons Chichester UK 2002

[50] T Mandjak and Z Veres ldquoThe D-U-C model and the stages ofthe project marketing processrdquo in Proceedings of the 14th IMPAnnual Conference Proceedings H K Nummela Ed pp 471ndash490 1998

[51] Q Meng Y Huang and R L Cheu ldquoCompetitive facilitylocation on decentralized supply chainsrdquo European Journal ofOperational Research vol 196 no 2 pp 487ndash499 2009

[52] H-M Hsu and W-P Wang ldquoDynamic programming fordelayed product differentiationrdquo European Journal of Opera-tional Research vol 156 no 1 pp 183ndash193 2004

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Mathematical Problems in Engineering

Hindawi Publishing Corporationhttpwwwhindawicom

Differential EquationsInternational Journal of

Volume 2014

Applied MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Mathematical PhysicsAdvances in

Complex AnalysisJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

OptimizationJournal of

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CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

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Operations ResearchAdvances in

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Function Spaces

Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of Mathematics and Mathematical Sciences

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

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Discrete Dynamics in Nature and Society

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

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Decision SciencesAdvances in

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Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Stochastic AnalysisInternational Journal of

2 Mathematical Problems in Engineering

study aims to IC design service firmrsquos resources integrationstrategic alliance and resources allocation We are utilizingthe IC design service firm for empirical case study to examinethe result of integrated resources This paper introducesthe De Novo programming in aid of criteria alternative forstrategic allianceresource allocation on IC design servicefirm [5 6]

The issue of research innovation has attracted scholarattention and interest in the last century Innovation varies interms of products services processes and business practices[7] Open innovationmeans that valuable ideasresources cancome internally or externally from company Chesbrough [8]points out ldquoopen innovationrdquo is a paradigm that assumes firmscan and should use external ideas as well as internal ideaand through internal or external paths output the outcomeof innovation technology to market The firms can throughauthorization offer intellectual property (IP) to partner fornew technology and also can spin-off new organization tomarket running new business This study will be based onopen innovation concept exploring the IC design servicefirm and how to integrate IC design industry resourcesand make optimal resources allocation in IC design servicefirm This model of De Novo programming is not onlyfor IC design service industry but also can apply to theother industrial implementation strategic allianceintegratingresource allocation

The remainder of this paper is organized as followsSection 2 discusses related theories of integrating resourceplanning model Section 3 details the construction of anefficient planning model for integrating resource Section 4utilizes De Novo programming approach illustrating anempirical case to demonstrate how the proposed integratingresource model for IC design service firm Section 5 presentsconclusions implications and recommendations

2 Theoretical Background

This section explores the related theoretical background ofintegrating resource planning model in the literature

21 Integrating Resource with Project Management Previousstudies have often explored integrating resource using theresource-based view (RBV) of firm organization [9 10]Holcomb and Hitt [11] describe tangible or intangible assetsowned or controlled by firms as resources and organiza-tional routines that allow firms to effectively integrate anduse resources allocation to implement on their strategiesplanning Resources have two key features (1) they mustenable the creation of organizationrsquos value and (2) thisresources provide unique support that can resist duplicativeefforts from the competitors [9] A firmrsquos competitiveness oroperational performance always is dependent on their coreresources it possesses [12]

Resource-based view of integration originated from thetheory of the growth of the firm [13] Barney [9] puts forwardamore contemporary interpretation of RBV emphasizing theimportance of resources in guiding firm activity transactioncost and managing a firmrsquos portfolio of capabilities as it

is central to enhancing competitive advantage [9 14] Thedefinition of RBV theory describes the resources as eithertangible or intangible and as both heterogeneous and imper-fectly mobile among firms [10] RBV theory has been studiedextensively in business strategy in the past decade and hasbecome a popular explanation of performance heterogeneityat the firm level [15 16] According to RBV theory firms seekcomplementary resource allocation to create synergies andacquire sustainable competitive advantages [17] To respondquickly to a dynamic environment firms should considerconstructing and extending limited resources to develop acapability for sustainable competitive advantage [18]

The pharmaceutical industry utilizes project manage-ment aspects of scheduling arranged and resource allocationon RampD pipelines that is a practical approach to improv-ing management performance [19] Project planning andscheduling arranged has become an important managementtool for todayrsquos complex investment environment [20 21]Project management can enhance resource allocation plan-ning quality and help organizations upgrade their perfor-mance [22] An efficient planning model that can aid firmsin achieving optimal resource allocation and consequentlythe best resource allocation outcome is not only desirable butnecessary for any organizationThrough projectmanagementconduct an effectively integrating resource planning modelfor IC design service firm of IC semiconductor industry isexpected

22 Through Alliance Planning Conduct Optimal ResourceAllocation Model Integrating IC design service humanresource into semiconductor industry has become an impor-tant issue in IC semiconductor industryMoore and Benbasat[23] suggest developing an instrument to help firms enhancetheir adoption of information technology innovation Theyregard firms as resource bundles for strategic alliance capa-bilities and competencies that provide a distinct sourceof competitive heterogeneity [24] Faced with insufficientresources firm should seek for more internal resourcesand leverage external resources that support firmrsquos researchdevelopment and can reallocate resources according to needsand goals of organization An efficient planningmodel wouldenhance the utilization rate of resources and improve RampDperformance Through alliance planning integrate internaland external resources of IC semiconductor industry that canenhance business operation performance

Competitive strategy is an important approach of man-agement issue Organizations considered competitive strat-egy very useful for resource planning and increasing effi-ciency across organizations management [25] Both compar-ative advantages in resources allocation and resource deploy-ment contribute to enhanced performance [26] To achievehigh performance of operational the top managers shouldprovide a conspicuous goal of strategic alliance direction [27]Strategic alliance planning is an approach of managementin defining a companyrsquos future progression direction anddeveloping a plan for its development in the future [28ndash31] Integrating IC design human resources into IC designservice through strategic alliances approach that can helpfirms improve business performance The IC design service

Mathematical Problems in Engineering 3

firm also can utilize strategy alliance planning to create muchmore added value on business operation

23 De Novo Programming Approach for Resource AllocationThe classical De Novo programming method proposed byZeleny [5 32 33] is an effective approach in dealing withoptimal design problems Currently system analysis anddesign have become important managerial and operationalissues in many countries and regions of water resources [34]Theoriginal idea ofDeNovo programming does not advocateproduction of individual or separate resources Speciallythe human resources are related and not independent ofIC design firms In the real world it is virtually impossibleto optimize all criteria when confronting a situation Theconcept of trade-off then becomes useful when consideringmultiple criteria andwhen operating under limited resourcesZeleny [35] regards trade-offs as properties of an inadequatelydesigned system and can thus be eliminated through design-ing a better preferably optimal system The De Novo pro-gramming approach can deal with an optimization problemsolution with multiple criteria Hence this approach wasadopted in this study to construct the integrating resourcesplanning model that can be universally applied to the othercase for different industries

24 Open Innovation Theory Open innovation has emergedas a new key theory in recent years It is a novel model fororganizing technological innovation in large RampD intensivefirms [36] According to open innovation model firmscan and should use both internal and external ideas andaccess the markets through both internal and external pathswhen they advance new technology [8] Open innovationoffers systematic incentives and explores a wide range ofinternal and external sources for innovative opportunitiesconsciously integrating such exploration with firmrsquos capabili-ties and resources and broadly exploiting those opportunitiesthroughmultiple channels [37] Open innovation has becomeincreasingly important for both practice and theory Organi-zations need shorter innovation cycles time and lower RampDcosts

The managerial challenges of open innovation involveutilizing external knowledge then identifying useful externalknowledge and integrating that knowledge into firm Forexample new products have significant trade-offs betweeninnovation speed in new product development costs andcompetitive advantage in relying on external knowledgerather than benefit in current internal knowledge [38] Theopen innovation phenomenon is reinforced by increasingglobalization of research technologies and innovation newinformation and communication technologies and poten-tials of new organizational forms and business models [39]

Chesbrough et al [36] show that the open innovationparadigm treats RampD as an open system in which valuableideas come from inside or outside of company and go tomar-ket from inside or spin-off from the firm (see Figure 1) Thisstudy combines the concepts of open innovation strategicalliance andDeNovo programming approach that integratesIC design service firmrsquos resource into IC semiconductorsindustry

3 Methodology

As seen in the above discussion integrated resources playsa central role in universal industry Specially in IC designservice firmrsquos allocates resource and promotes sustainabilityefforts Next section explores the construction of an efficientplanningmodel for strategy alliance alternative to integratingresources using De Novo programming

31 Integrating Resource Allocation The semiconductorindustry face too many challenge including cost downcompetitor treat human resource constraint and otherresources limitation which in turn affects IC designeffectiveness and business operational performance Optimalhuman resource allocation bymathematic programming thusbecomes a key issue in IC design service firms Mathematicprogramming distributes limited resources to competingactivities to achieve optimal resource allocation Linearprogramming is the most popular mathematic programmingapproach Kantorovich and Koopmans [40] develop linearprogramming formula which can be described as follows

Max Cx

st Ax le b

x ge 0

(1)

Here C = C119902times119899 and A = A119898times119899 are matrices b = (1198871

119887119898)119879isin 119877119898 and x = (119909119894 119909119895 119909119899)

119879isin 119877119899 Let the 119896th

row of119862 be denoted by119862119896 = (119888119896119894 119888119896

119895 119888119896119899) isin 119877119899 and119862119896119909

(119896 = 1 119902) is the 119896th evaluation criterion or alternativeobjective function This linear programming problem canbe solved in several ways which include using the simplexmethod or the interior-point algorithm that allocates lim-ited resource problems Although mathematic programmingoffers a solution method to the resource allocation problemsit is unreasonable to assume additivity when extending thismethod to manage an alliance resourceThis situation is baseon additivity which presumes that all productive elementsare independent and the total effects equal the summationof each individual effect Such assumption does not fit firmrsquosneeds when the firm expects to create a synergies effect ofbusiness operations

The concept of emerging mass customization has beenproposed for solving the problem of element independenceGenerally a company can obtain profits in twoways One is toincrease revenue with higher unit price through customizingand the other is to reduce the cost of unit through max eco-nomic scale of production With element independence it isimpossible to reduce cost of unit and increase revenue simul-taneously Utilizing the concept of mass customization canrelease limited element independence This study assumesthat there exists a market alliance between companies 119860and 119861 with 120587119860 and 120587119861 denoting their respective profits Thegoal of a company is to maximize profits and the feasiblesolutions are within the space surrounded by dotted linesshown in Figure 2 Compromise solutions are typically thebest decision in traditional mathematic programming and

4 Mathematical Problems in Engineering

Licensing

Technology spinoffsInternal

technology

base

External

technology

base

Other firmrsquos

New

Current

R D

Technology insourcing

market

market

market

Figure 1 The open innovation paradigm (sources from [36])

0

Max

Max

Feasible space

120587A A C

B

120587B

(a)

A C

B

(b)

Figure 2 Feasible options obtained using linear programming

they fall into 119860 cap 119861 Options space contained in points 119860119861 and 119862 that include the ideal point 119862 they are unavailableoptions space what caused with utilizing linear programmingadditivity

According to the assumption of additivity combiningalliance resources allows not only 1 + 1 = 2 but also can obtain1 + 1 gt 2 Therefore synergies concept are popular reasonsfor obtaining optima result through resource alliances [41] Inother words traditionalmathematic programming is rationaland available when a firm has resource constraints thatcannot change if it is produced individually [42] Howeverthe traditionalmethod is no longer suitable when redesigningsystems and the optimal solution was adopted throughstrategic alliances with difference firms

This research uses De Novo programming to releaselimited element independence and to solve the problem ofan optimal resource allocation portfolio through resourcealliances to achieve the aspiration goal

32 De Novo Programming for Alliances Approach The DeNovo programming is one of MCDM method that canremodify the systems to achieve an aspiration goal of firmrsquosexpectation By releasing various constraints the De Novoprogramming attempts to break limitations to achieve theoptimal solution Through formation strategic alliance andresource integration the current work extends the De Novoprogramming to obtain an optimal solution [43ndash45] TheDe Novo perspective combines transaction cost theory andresource-based view (RBV) to provide a holistic perspectivefor achieving an aspiration level [46] In this researchfirm seeks strategic allianceresource integration accordingto firmrsquos needs If the minimum alliance cost lies betweendesign cost and human resource cost Semiconductorrsquos firmshould seek strategic allianceresource integrated for ICdesign human resources sharing The IC design service costcan be modified as if allianceintegration cost is less thanindividual firmrsquos cost summary (le sum119873

119894=1)

Mathematical Problems in Engineering 5

The firm should seek allianceintegration with the otherpartner

From the RBV view firms seek resource alliances capa-bilities by allying with a partner to create synergies based onRBV theory The rule of the RBV can also be modified asif allianceintegration benefit is larger than individual firmrsquosprofit summary (ge sum119873

119894=1)

The firm should seek allianceintegration with the otherfirms

Now we involve the cost and RBV theory into the DeNovo programming if the firm alternatives are based on twodifferent resources 119878 and 119879 the rule of resource integratingcan be expressed as follows

if 119864 (119878 cup 119879) minus 119880 (119862119878119879) gt 119864 (119878) + 119864 (119879) minus 119880 (119862119878) minus 119880 (119862119879) (2)

then the firm should seek resource integration with partnerof industry cluster

Also it can express a general formula as follows

if 119864 (1198781 cup 1198782 sdot sdot sdot cup 119878119873) minus 119880 (119862 alliance cost )

ge

119873

sum119894=1

[119864 (119878119894) minus 119880 (119878119894)] 119894 = 1 2 119873(3)

where119864(sdot) is the benefit function119880(sdot) is the cost function119862119878and 119862119879 denote the total design cost in 119878 and 119879 respectivelyand 119862119878119879 denotes the allianceintegration cost between 119878

and 119879 The probability of 119878 and 119879 events seeking strategicallianceresource integration can be demoted respectively as

119901 (119904) = 1 120582119864 (119878 cup 119879) minus 120579119880 (119862119878119879) gt 119864 (119878) minus 119880 (119862119878)

0 120582119864 (119878 cup 119879) minus 120579119880 (119862119878119879) lt 119864 (119878) minus 119880 (119862119878)

(4a)

119901 (119879) =

1 (1 minus 120582) 119864 (119878 cup 119879) minus (1 minus 120579)119880 (119862119878119879)

gt 119864 (119879) minus 119880 (119862119879)

0 (1 minus 120582) 119864 (119878 cup 119879) minus (1 minus 120579)119880 (119862119878119879)

lt 119864 (119879) minus 119880 (119862119879)

(4b)

where 120582 denotes the percentage of increasing alliance benefitin 119878 and 120579 denotes the percentage of reducing alliance costin 119878 Now we can involve De Novo programming into theplanning model of resource integrating that can be expressedas

max 119864 (119878 cup 119879) minus 119880 (119862119878119879)

st 119908119909 le 119861 119909 ge 0(5)

where 119908 = 119901119860 = (1199081 119908119899) isin 119877119899 and 119901 = (1199011 119901119898) isin

119877119898 and 119861 isin 119877 present the unit price of resources and the totalavailable budget Then the knapsack solution is

119909lowast= [0

119861

119862119896 0]

119879

(6)

where

119888119896

119888lowast119896

= max119895

(119888119895

119888lowast119895

) (7)

Max 0

Max

Feasiblespace

120587R

120587AA

C

B

Figure 3 Feasible options obtained using De Novo programming

The optimal solution to (5) is given by (6) and

119887lowast= 119860119909lowast (8)

The final allianceintegration benefit (Ψ(119878lowast)) in 119878 is

Ψ (119878lowast) = 1198941015840119887lowast119880(minus119862119878119879) (9)

where 119894 is the identity column vector According to (9) wecan judge whether or not the firm should seek strategicallianceresource integration by (3) (4a) and (4b) Further-more using De Novo programming we can easily achieveoptimal resource allocation and create synergies betweenalliances The difference between traditional mathematicprogramming andDeNovo programming lies in the ability oftheDeNovoprogramming to redefine its boundaries throughsystem redesign reconfiguration or reshaping [33] Figure 3shows the difference in feasible options space obtainedthrough De Novo programming method

The greatest difference between Figures 2 and 3 is thatthe unavailable solutions before were made available throughDe Novo programming now In other words the idealpoint 119862 is the goal of optimal solution through strategicallianceresource integrating approach

4 Empirical Study Practice

It is commonly believed that integrating IC design humanresources into IC semiconductor supply chain system canenhance operation performance and such integration wouldrequire a complete planning model to ensure effectivenessThe proposed planning model is applied to IC design servicefirms in Taiwan as a empirical case study to examine theplanning model feasibility

41 The Case Study of Taiwanrsquos IC Service Design FirmTaiwanrsquos semiconductors industry is a globalization businessspecializing as the worldrsquos number one of DRAM IC foundrymanufacturer The IC semiconductors industry is a completesupply chain that includes IC design service supplier andcustomer The IC supply chain system developed becamefirms cluster with difference supply chain speciality needs

6 Mathematical Problems in Engineering

IC design service

Supplier Customer

Servicecluster

Suppliercluster

Customercluster

Figure 4 The supply chain of IC semiconductor industry

(see Figure 4) The IC design service is one of semiconductormanufacturing processing before launching into IC foundryThe IC design service firms obtain the order from customerand through project management controls the design sched-ule for customer needs More than 365 IC design servicefirms have been established around Hsinchu Science Parkin Taiwan This IC service design firm cluster situation isvery special in the world Consequently studying Taiwanrsquos ICdesign service firms is a very interesting empirical case

42 The Problems of IC Semiconductor Industry CurrentlyTaiwanrsquos semiconductors face a very slow economics situationof business operation Government release encourage policytry to save the semiconductor industry but these policies wasfail The economics of semiconductor continues going downof economic curve The IC industry external environmentalfactors are multiples and dynamics specializing on theIC design service sector Also the IC design service firmrequires one high performance project management to helpmanage related task Integrating internal human resource andleveraging external resource and adjusting business strategyformatch customer needs of IC designmarket As such firmsshould offer just-in-time mechanism of services for productsdelivery and quick response system for customers A little losson service process could bring the result of lose customerTherefore IC design firm should determine to operate atlowest cost and high quality level for IC design service Highperformance IC design service systems should be consideredin the semiconductor supply chain system

43 IC Design Service Firms The IC design skill is one of keytechnical of IC semiconductor manufacturing system It is

a new business model emerging as a vertical disintegrationproduct in the IC semiconductor industry The IC semicon-ductor combines multiprocesses into one complete systemincluding IC design and an IDM (integrated device manu-facturer) fabrication assembly test in the 1970s IC-designsand IDM were separated in the 1980s from one complete setsystem to develop two subsystems of IC design and IDMfabrication and continues developing including IC designand IDM fabrication and assembly The IC semiconductorindustry after the 1980s modified processing to include thesystem IC design IDM fabrication IC foundry IC assemblyand IC test on the subsystem The IC design subsystem after2001 extends from one to three parts including SiP (systemin package) design system and IC design The IC designservice firm has become an important process of the ICsemiconductors industry Figure 5 shows IC design servicedevelopment trajectory from 1960 to 2013

The IC design service firm is a high technology knowl-edge industry The IC design service firm requires pro-fessional human design who must have engineering back-ground specialized design technology knowledge and alsoneed high speed internet networks a convenient communi-cation digital platform and a high operation performanceThis study expects a building of an MCDM model with DeNovo programming for integrating the resource of IC designservice firmsThe IC design service firm is multidimensionaland highly competitive focusing on special professionaltechniques and quick response of design services De Novoprogramming helps to plan service model for achievingaspiration levels of the IC design service firms

44 The Relationship of IC Design Service Supplier andCustomer The IC semiconductors industry is a multipleproduction process that includes design fabrication foundrytesting assembly and delivery of products to customer TheIC design service is a substructure under the IC supplierproviding IC design service for supplier or customer The ICdesign service firms offer system in package (SiP) design todirect customer or IC design and IC layout design for supplier(see Figure 6) The IC design service firms offers value addedat each design service process for example highlightingperformance powerful function low production cost systemon chip (SoC) and redownsizing IC design space Figure 6shows relationship between of IC design service supplier andcustomer

The IC design service firms not only provides designservice for IC semiconductors manufacturing systems butalso offers new innovative ideasfunction on IC chip forcustomer The IC design service firms generally providesprofessional IC design knowledge and IC foundry techniquesthat can obtain greater economics efficiency of semiconduc-tors production the result should be increased IC design ser-vice business opportunity Consequently adopting innovativeideas becomes a very important task in IC design serviceprocessing

45 Through De Novo Programming Achieving AspirationGoal This study expects through De Novo programming to

Mathematical Problems in Engineering 7

After 20001980s to 1990sBefore 1970s

IC design

System IC-ASSP IC-ASIC SOC-IP

SystemSystemSystemSystemSystemIC designIDM fabassembly

test

DS

IC design

IDM fab

Foundry

Assembly

Test

IC design

IDM fab

Foundry

Assembly

Test

IC design

IDM fab

Assembly

SystemIC designIDM fabassembly

test

20001990198019701960 2013

SIP

Figure 5 The IC design service development trajectory from 1960 to 2013 year

IC designservice

Supplier CustomerFinished products

SiP design service

Design requirement IC design service

Order requirement

IC layout order

Figure 6 The relationship of IC design service supplier andcustomer

achieve optimal resource integrating purpose The IC designservice firm must consider resource constrain under mul-tiple objectives alternatives before making decisions Firmface multiple objectives and evaluation criteria of problemnamely dealing with resources integrating and adjustingbusiness strategic to achieve the aspiration goal of businessIn general through trade-off skill it is almost impossible toobtain optimization of all criteria of a given system Zeleny[32] states the new system can eliminate the resource con-strain through redesign then preferably obtain optimizationresults what can improves theTrade-off problems Zeleny [35]proposes the optimal portfolio resource allocation conceptwhich designs a resource integrated system that is individualresource levels cannot determine separately so new designsystems do not have trade-offs to consider Zeleny [33]develops a De Novo programming method for designing an

Low

High

High

For achieving the aspireddesired

level

Good

The best

The best

Efficiencies

Perfo

rman

ce

Figure 7Through De Novo programming achieving the aspirationgoal

optimal system by reshaping the feasible set [33] Firm canmake strategy alliances with partner of supply chain systemand achieves resource integration adjusting the businessgoal remodifying the business model and justifying targetcustomer services This study expects through De Novo pro-gramming approach to compute the data of resource integrat-ing and justify the resource allocationobjective alternativefrom a good level to best level thus achieving aspiration goal(see Figure 7)

8 Mathematical Problems in Engineering

46 Empirical Case Study

461 A Case Study of IC Production Firm A general case ofIC production problem involving two IC products GPS andcell phone of IC in quantities 1199091 and 1199092 each consuming fivedifferent resources (unitmarket prices of resources are given)The data is summarized as shown in Table 1

The costs of the given resources portfolio

(28 times 32) + (35 times 30) + (10 times 55)

+ (18 times 10) + (10 times 28) = $2956(10)

Unit costs of producing one unit goods of the two products

1199091 = (28 lowast 6) + (35 lowast 3) + (10 lowast 14) + (18 lowast 2)

+ (10 lowast 6) = $509

1199092 = (28 lowast 2) + (35 lowast 5) + (10 lowast 6) + (18 lowast 5)

+ (10 lowast 8) = $461

(11)

Expected profit margins (price-cost) arethe profit of 1199091 product = $549 minus $509 = $40unitthe profit of 1199092 product = $491 minus $461 = $30unit

Maximizing total value of function 11989111198911 = 401199091 + 301199092 (12)

Maximizing total quality index 11989121198912 = 81199091 + 101199092 (13)

Maximizing levels of two products calculated by De Novoprogramming

max 1198911 = 401199091 + 301199092

max 1198912 = 81199091 + 101199092

st 61199091 + 21199092 le 32

31199091 + 51199092 le 30

141199091 + 61199092 le 55

21199091 + 51199092 le 10

61199091 + 81199092 le 28

1199091 1199092 ge 0

(14)

Maximum 1198911 in profit

max 1198911 997888rarr 1199091 = 371

1199092 = 052

119891lowast

1= 40 times 371 + 30 times 052 = $164

(15)

Maximum 1198912 in total quality index

max 1198912 997888rarr 1199091 = 371

1199092 = 052

119891lowast

2= 8 times 371 + 10 times 052 = $3488

(16)

Minimizing the total cost by considering the followingconstraints

min 5091199091 + 4611199092

st 401199091 + 301199092 le 164

81199091 + 101199092 le 3488

(17)

Maximum 1198911 in profit

max 1198911 997888rarr 1199091 = 371

1199092 = 052

119891lowast

1= 40 times 371 + 30 times 052 = $164

(18)

Maximum 1198912 in total quality index

max 1198912 997888rarr 1199091 = 371

1199092 = 052

119891lowast

2= 8 times 371 + 10 times 052 = $3488

(19)

Cost of the newly designed system

(28 times 2328) + (35 times 1368) + (10 times 5506)

+ (18 times 1002) + (10 times 2642) = $21258(20)

The new portfolio of resources proposed by the consultant isshown in Table 2

The result of data analysis shows that the new planningsystem cost is $21258

The given resource portfolio cost is $2956 Comparisonbetween original and new planning model we found outthat the new planning model can reduce cost to $8305 andthat new resource planning model is one reliable modelConsequently the IC design service firm can follow the newplanningmodel ofDe novo programming to process resourceintegration

462 The Case of IC Design Service Firm The IC designservice firm is a professional workshop that focuses on theIC design task dependent on what design they can provideservice The IC design service task is also a project-basedplanning under a large RampDproject with business developingstrategy A project-based firm uses external delivery projectsfor business purposes [47 48] This study utilizes a real caseof IC design project management to practice on IC designservice firm Project planning may include many subprojectsthat depend on different cases of customerrsquos demand Centralfeatures of project management were identified according toindividual project uniqueness human resources of projectand business network complexity discontinuity of demandand relationships between other projects and considerablefinancial commitment of the parties [49 50] Conductingaspiration project planning under resource constraint is atarget issue for the IC design service firm This real caseincludes five different resources which included humanpower resource design equipments design materials special

Mathematical Problems in Engineering 9

Table 1 The IC of GPS and cell phone production requiring material

Unit price ($) Material of production processTechnological coefficients

Resource portfolio(units)(Resource requirement)

1199091

1199092

28 Silver wire 6 2 3235 Golden wire 3 5 3010 Silicon Crystal 14 6 5518 Chemistry material 2 5 1010 Conductor material 6 8 28

Table 2 The new resource portfolio planning of IC of GPS and cell phone

Unit price ($) Material of production processTechnological coefficients

Resource portfolio(units)(Resource requirement)

1199091

1199092

28 Silver wire 6 2 232835 Golden wire 3 5 136810 Silicon Crystal 14 6 550618 Chemistry material 2 5 100210 Conductor material 6 8 2642

technical human and design outsourcing with two differentproject teams for the A company customer to performthe IC design task The current study names the twoprojects as the 1198751 and 1198752 project team programs This realplanning program case utilizes the De Novo programmingfor adjustable resource allocation and seeks out minimumcost with maximum performance This work transitions thefive kinds of resources (design equipments design humanpower design material special technical human and design-outsourcing) in the case study to dollar amounts for easy costcalculation Design equipments cost is $380 per unit designhuman power labor cost is $240 per unit design materialresource cost is $200 special technical human cost is $260per unit and design outsourcing cost is $120 per unit

Costs of given resources portfolio

(380 times 26) + (240 times 50) + (200 times 30) + (120 times 10)

+ (260 times 16) = $33240(21)

Unit costs for running each project of the two projects

1198751 = (380 lowast 8) + (240 lowast 10) + (200 lowast 4)

+ (260 lowast 6) = $7800

1198752 = (380 lowast 6) + (240 lowast 4) + (200 lowast 4)

+ (120 lowast 4) = $4520

(22)

Expected profit margins (price cost) are

1198751 = $8200 minus $7800 = $400unit

1198752 = $4820 minus $4520 = $300unit(23)

Maximizing total value of function 1198911

1198911 = 4001199011 + 3001199012 (24)

Maximizing total quality index 1198912

1198912 = 81199011 + 101199012 (25)

Maximizing levels of the two projects calculated by De Novoprogramming

max 1198911 = 4001199011 + 3001199012

max 1198912 = 81199011 + 101199012

st 81199011 + 61199012 le 26

101199011 + 41199012 le 50

41199011 + 41199012 le 30

41199012 le 10

61199011 le 16

1199011 1199012 ge 0

(26)

Maximum 1198911 in profit

max 1198911 997888rarr 1199011 = 267

1199012 = 078

119891lowast

1= 400 times 267 + 300 times 078 = $1300

(27)

10 Mathematical Problems in Engineering

Table 3 1198751and 119875

2project team program require resource of IC design service

Unit price ($) Resources of IC designProject coefficients

Resource portfolio(units)

(Resource requirement)1198751

1198752

380 Design equipments 8 6 26240 Human power resource 10 4 50200 Design material 4 4 30120 Design outsource 0 4 10260 Special technical human 6 0 16

Table 4 The new resource portfolio planning model of 1198751and 119875

2project team program

Unit price ($) Resources of IC designProject coefficients

Resource portfolio(units)(Resource requirement)

1198751

1198752

380 Design equipments 8 6 26240 Human power resource 10 4 325200 Design material 4 4 13120 Design outsource 0 4 0260 Special technical human 6 0 195

Maximum 1198912 in total quality index

max 1198912 997888rarr 1199011 = 267

1199012 = 078

119891lowast

2= 8 times 267 + 10 times 078 = $2916

(28)

Minimizing the total cost by considering the followingconstraints

min 78001199011 + 45201199012

st 1198911 = 4001199011 + 3001199012 le 1300

1198912 = 81199011 + 101199012 le 2916

(29)

Maximum 1198911 in profit

max 1198911 997888rarr 1199011 = 325

1199012 = 0

119891lowast

1= 400 times 325 = $1300

(30)

Maximum 1198912 in total quality index

max 1198912 997888rarr 1199011 = 325

1199012 = 0

119891lowast

2= 8 times 325 = $26

(31)

Cost of new design planning

(380 times 26) + (240 times 325) + (200 times 13) + (260 times 195)

= $25350(32)

New portfolio of resources proposed by the consultant isshown in Table 4

47 Discussions

471 The General Case of IC Production Firm The datashow the empirical case study using the De Novo program-ming which remodifies the planning model and adjusts theresources allocation portfolio The new plan model offersthe best optimized resource integration and reduces losingand enhances firmrsquos competitiveness Technical problemsprogramming are discussed as follows

(i) The original plan needs a total cost of $2956 withgiven resources portfolios

[(28 times 32) + (35 times 30) + (10 times 55) + (18 times 10)

+ (10 times 28) = $2956] (33)

(ii) The new planning model of resource allocation onlyrequires cost of $21258

[(28 times 2328) + (35 times 1368) + (10 times 5506)

+ (18 times 1002) + (10 times 2642) = $21258] (34)

(iii) The newly planning model can save $8302 costConsequently firm seeks new planning model tointegrate resource

$2956 minus $21258 = $8302 (35)

(iv) The newly planning model use the De Novo pro-gramming to reduce resource loss and get betterperformance than the original planning model Thispaper also utilizes the empirical case examination ofDe Novo programming for IC design service firmperformance The new planning model obtains muchbetter resource integration

Mathematical Problems in Engineering 11

472 The Case of IC Design Service Firm Firms have massproduction capacity limitation per one time period due tolimited resources especially for the preceding competitivefacility location problem on the decentralized supply chain[51] The IC design service firm is a standard project-basedprogrammodel Many project teams program through coop-eration under a large design program for different customerare needed This study utilizes a case study of IC design firmfor planning an IC design to customer serviceThis case studyincludes two project teams The 1198751 and 1198752 project with twodifferent plan programming on IC design see (Table 3) Oneproject team leader prefers hiring a special technical engineerfor the IC design task that can maintain the IC design keytechnician and train other staffThe other project team leaderprefers outsourcing design that reduces cost The IC designservice firm chooses the alternative optimal project teambased on the result of De Novo programming

The resource portfolio of firmrsquos original cost is as follows

(380 times 26) + (240 times 50) + (200 times 30) + (120 times 10)

+ (260 times 16) = $33240(36)

The cost of newly planning model through the De Novoprogramming is as follows

(380 times 26) + (240 times 325) + (200 times 13) + (260 times 195)

= $25350(37)

The new planning model saves cost as follows

$33240 minus $25350 = $7890 (38)

The IC design firm adopting1198751 project team cost is as follows

1199011 (8 times 26) + (10 times 325) + (4 times 13) + (6 times 195) = $702(39)

The IC design firm adopting1198752 project team cost is as follows

1199012 (6 times 26) + (4 times 325) + (4 times 13) = $338 (40)

The result of data analysis shows through the De Novoprogramming method the IC design service firm that canfind out which project team is the optimal solution In thisempirical case adopting 1198752 project team can save design costmuchmore than1198751 projectThe new planningmodel reduces$7890 in cost compared with the original planning modelSo the newly planning model is the best choice for achievingaspiration goal

5 Conclusion

The IC semiconductor industry faces uncertainty anddynamic external environment especially at IC design servicefirm that needs one resource integrating programmingmodelfor new product development [52] Adopting an efficientstrategic alliancesresource integrating planning model is akey issue for achieving firmrsquos business goals especially astheir resource was constrainedThe present study considers a

planningmodel based on resource constraintsThis study uti-lizes the De Novo programming for achieving firmrsquos strategicgoal The De Novo programming can help firms find out anoptimal resource integration base on firm financial considerThis planning model provided reliable systematize approachthat combines alliances from external resources and changethem to internal resources application The project team1198752 adopting open innovation concept through outsourcingreduces design cost and enhances design performance DeNovo programming not only obtains optimal resource inte-gration but also promotes the conceptual of strategic alliancesfor firmrsquos cooperation between each other

51 Management Implication This research aims to create aresource integrating plan model through IC design servicefirm as an empirical case for examining the result of theplanning model The planning model is based on openinnovation theory exploring the IC design service firmwhichintegrates IC design resources and makes optimal resourcesintegrate in IC design service firm The planning model ofDe Novo programming is not only for IC design service firmbut also can apply to the other industrial implementationstrategic allianceresource integration This planning modelis a universal model for the other industry fields

52 Suggestion The IC design service industry is a veryspecial industry We suggest the extend to the other industrythat can find out different results Suggestion utilizes the otherapproach of MCDMmethod exploring different case studiesof industry

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

The authors appreciate two blind reviewers who have pro-vided very excellent comments These comments are verygood for the authors to improve the quality of the paperAlso thanks to the editor Professor Chang for offering lots ofexcellent suggestions that helped enhance this paperrsquos quality

References

[1] R Varadarajan ldquoOutsourcing think more expansivelyrdquo Journalof Business Research vol 62 no 11 pp 1165ndash1172 2009

[2] G Forgionne and Z Guo ldquoInternal supply chain coordinationin the electric utility industryrdquo European Journal of OperationalResearch vol 196 no 2 pp 619ndash627 2009

[3] R George and R Kabir ldquoBusiness groups and profit redistribu-tion a boon or bane for firmsrdquo Journal of Business Research vol61 no 9 pp 1004ndash1014 2008

[4] L Duckstein and S Opricovic ldquoMultiobjective optimization inriver basin developmentrdquoWater Resources Research vol 16 no1 pp 14ndash20 1980

12 Mathematical Problems in Engineering

[5] M Zeleny ldquoA case study in multiple objective design DeNovo programmingrdquo inMultiple Criteria Analysis OperationalMethods vol 1 pp 37ndash52 1981

[6] M Zeleny ldquoOptimal system design with multiple criteria DeNovo programming approachrdquo Engineering Costs and Produc-tion Economics vol 10 no 1 pp 89ndash94 1986

[7] S Christophersona M Kitsonb and J Michiec ldquoInnovationnetworks and knowledge exchangerdquo Cambridge Journal ofRegions Economy and Society vol 1 no 2 pp 165ndash173 2008

[8] H W Chesbrough Open Innovation The New Imperativefor Creating and Profiting from Technology Harvard BusinessSchool Press Boston Mass USA 2003

[9] J B Barney ldquoFirm resources and sustained competitive advan-tagerdquo Advances in Strategic Management vol 17 pp 203ndash2272000

[10] B Wernerfelt ldquoA resource-based view of the firmrdquo StrategicManagement Journal vol 5 no 2 pp 171ndash180 1984

[11] T R Holcomb and M A Hitt ldquoToward a model of strategicoutsourcingrdquo Journal of Operations Management vol 25 no 2pp 464ndash481 2007

[12] D J Collis ldquoA resource-based analysis of global competitionthe case of the bearings industryrdquo Strategic Management Jour-nal vol 12 no 1 pp 49ndash68 1991

[13] E Penrose The Theory of the Growth of the Firm John Wileyand Sons New York NY USA 1959

[14] E Anderson andH Gatignon ldquoModes of foreign entry a trans-action cost analysis and propositionsrdquo Journal of InternationalBusiness Studies vol 17 no 3 pp 1ndash26 1986

[15] J Chimhanzi and R E Morgan ldquoExplanations from themarketinghuman resources dyad formarketing strategy imple-mentation effectiveness in service firmsrdquo Journal of BusinessResearch vol 58 no 6 pp 787ndash796 2005

[16] J Fahy ldquoA resource-based analysis of sustainable competitiveadvantage in a global environmentrdquo International BusinessReview vol 11 no 1 pp 57ndash78 2002

[17] M A Harrison R E Hoskisson and D Ireland ldquoSynergies andpost acquisition performance differences versus similarities inresource allocationsrdquo Journal of Management vol 17 no 1 pp173ndash190 1991

[18] D J Teece G Pisano and A Shuen ldquoDynamic capabilities andstrategic managementrdquo Strategic Management Journal vol 18no 7 pp 509ndash533 1997

[19] V A Varma J F Pekny G E Blau and G V Reklaitis ldquoAframework for addressing stochastic and combinatorial aspectsof scheduling and resource allocation in pharmaceutical RampDpipelinesrdquoComputers andChemical Engineering vol 32 no 4-5pp 1000ndash1015 2008

[20] R Kolisch ldquoEfficient priority rules for the resource-constrainedproject scheduling problemrdquo Journal of Operations Manage-ment vol 14 no 3 pp 179ndash192 1996

[21] R Kolisch and S Hartmann ldquoExperimental investigationof heuristics for resource-constrained project scheduling anupdaterdquo European Journal of Operational Research vol 174 no1 pp 23ndash37 2006

[22] M J Leiblein ldquoThe choice of organizational governance formand performance predictions from transaction cost resource-based and real options theoriesrdquo Journal of Management vol29 no 6 pp 937ndash961 2003

[23] G CMoore and I Benbasat ldquoDevelopment of an instrument tomeasure the perceptions of adopting an information technologyinnovationrdquo Information Systems Research vol 2 no 3 pp 192ndash222 1991

[24] R E Hoskisson M A Hitt W P Wan and D Yiu ldquoTheoryand research in strategic management swings of a pendulumrdquoJournal of Management vol 25 no 3 pp 417ndash456 1999

[25] C C Miller and L B Cardinal ldquoStrategic planning and firmperformance a synthesis of more than two decades of researchrdquoAcademy of Management Journal vol 37 no 6 pp 1649ndash16651994

[26] S D Hunt A General Theory of Competition Sage ThousandOaks Calif USA 2000

[27] S Hart and C Banbury ldquoHow strategy-making processes canmake a differencerdquo Strategic Management Journal vol 15 no 4pp 251ndash269 1994

[28] E Drakopoulos ldquoEnterprise network planning and designmethodology and applicationrdquo Computer Communications vol22 no 4 pp 340ndash352 1999

[29] V Kumar B Maheshwari and U Kumar ldquoERP systems imple-mentation best practices in Canadian government organiza-tionsrdquoGovernment InformationQuarterly vol 19 no 2 pp 147ndash172 2002

[30] W R Lewis ldquoStrategic planningrdquo Hospital Materiel Manage-ment Quarterly vol 10 no 4 pp 57ndash63 1989

[31] D Wainwright and T Waring ldquoThree domains for imple-menting integrated information systems redressing the bal-ance between technology strategic and organisational analysisrdquoInternational Journal of InformationManagement vol 24 no 4pp 329ndash346 2004

[32] M Zeleny ldquoOptimal system design with multiple criteria DeNovo programming approachrdquo Engineering Costs and Produc-tion Economics vol 10 no 1 pp 89ndash95 1986

[33] M Zeleny ldquoOptimal given system versus designing optimalsystem the De Novo programming approachrdquo InternationalJournal of General System vol 17 no 3 pp 295ndash307 1990

[34] Y M Zhang G H Huang and X D Zhang ldquoInexact de Novoprogramming for water resources systems planningrdquo EuropeanJournal ofOperational Research vol 199 no 2 pp 531ndash541 2009

[35] M Zeleny ldquoTrade-offs free management via De Novo pro-grammingrdquo International Journal Operations and QuantitativeManagement vol 1 no 1 pp 3ndash13 1995

[36] H Chesbrough W Anhaverbeke and J West Eds Openinnovation Researching a New Paradigm Oxford UniversityPress 2006

[37] J West and S Gallagher ldquoChallenges of open innovation theparadox of firm investment in open-source softwarerdquo R amp DManagement vol 36 no 3 pp 319ndash331 2006

[38] E H Kessler P E Bierly and S Gopalakrishnan ldquoInternalversus external learning in new product development effects onspeed costs and competitive advantagerdquo R amp D Managementvol 30 no 3 pp 213ndash223 2000

[39] O Gassmann and E Enkel ldquoTowards a theory of open innova-tion three core process archetypesrdquo in Proceedings of the RampDManagement Conference (RADMA rsquo04) Lisbon Portugal 2004

[40] L V Kantorovich and T C Koopmans Problems of Applicationof Optimization Methods in Industry Federation of SwedishIndustry 1976

[41] T K Das and B-S Teng ldquoA resource-based theory of strategicalliancesrdquo Journal of Management vol 26 no 1 pp 31ndash61 2000

[42] Z Babic and I Pavic ldquoMulticriterial production planning byDe Novo programming approachrdquo International Journal ofProduction Economics vol 43 no 1 pp 59ndash66 1996

[43] J K C Chen and G H Tzeng ldquoPerspective strategic allianceand resource allocation in supply chain systems through the De

Mathematical Problems in Engineering 13

Novo programming approachrdquo International Journal Sustain-able Strategic Management vol 1 no 3 pp 320ndash339 2009

[44] J-J Huang G-H Tzeng and C-S Ong ldquoMotivation andresource-allocation for strategic alliances through the De NovoperspectiverdquoMathematical and ComputerModelling vol 41 no6-7 pp 711ndash721 2005

[45] J-J Huang G-H Tzeng and C-S Ong ldquoChoosing bestalliance partners and allocating optimal alliance resources usingthe fuzzymulti-objective dummyprogrammingmodelrdquo Journalof the Operational Research Society vol 57 no 10 pp 1216ndash12232006

[46] R M Grant ldquoThe resource-based theory competitive advan-tage implications for strategy formulationrdquo California Manage-ment Review vol 33 no 3 pp 114ndash135 1991

[47] K A Artto and K Wikstrom ldquoWhat is project businessrdquoInternational Journal of Project Management vol 23 no 5 pp343ndash353 2005

[48] J Soderlund ldquoOn the broadening scope of the research onprojects a review and a model for analysisrdquo InternationalJournal of ProjectManagement vol 22 no 8 pp 655ndash667 2004

[49] B Cova P Ghauri and R Salle Project Marketing BeyondCompetitive Bidding JohnWiley amp Sons Chichester UK 2002

[50] T Mandjak and Z Veres ldquoThe D-U-C model and the stages ofthe project marketing processrdquo in Proceedings of the 14th IMPAnnual Conference Proceedings H K Nummela Ed pp 471ndash490 1998

[51] Q Meng Y Huang and R L Cheu ldquoCompetitive facilitylocation on decentralized supply chainsrdquo European Journal ofOperational Research vol 196 no 2 pp 487ndash499 2009

[52] H-M Hsu and W-P Wang ldquoDynamic programming fordelayed product differentiationrdquo European Journal of Opera-tional Research vol 156 no 1 pp 183ndash193 2004

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Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Mathematical Problems in Engineering

Hindawi Publishing Corporationhttpwwwhindawicom

Differential EquationsInternational Journal of

Volume 2014

Applied MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

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Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Mathematical PhysicsAdvances in

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Mathematical Problems in Engineering 3

firm also can utilize strategy alliance planning to create muchmore added value on business operation

23 De Novo Programming Approach for Resource AllocationThe classical De Novo programming method proposed byZeleny [5 32 33] is an effective approach in dealing withoptimal design problems Currently system analysis anddesign have become important managerial and operationalissues in many countries and regions of water resources [34]Theoriginal idea ofDeNovo programming does not advocateproduction of individual or separate resources Speciallythe human resources are related and not independent ofIC design firms In the real world it is virtually impossibleto optimize all criteria when confronting a situation Theconcept of trade-off then becomes useful when consideringmultiple criteria andwhen operating under limited resourcesZeleny [35] regards trade-offs as properties of an inadequatelydesigned system and can thus be eliminated through design-ing a better preferably optimal system The De Novo pro-gramming approach can deal with an optimization problemsolution with multiple criteria Hence this approach wasadopted in this study to construct the integrating resourcesplanning model that can be universally applied to the othercase for different industries

24 Open Innovation Theory Open innovation has emergedas a new key theory in recent years It is a novel model fororganizing technological innovation in large RampD intensivefirms [36] According to open innovation model firmscan and should use both internal and external ideas andaccess the markets through both internal and external pathswhen they advance new technology [8] Open innovationoffers systematic incentives and explores a wide range ofinternal and external sources for innovative opportunitiesconsciously integrating such exploration with firmrsquos capabili-ties and resources and broadly exploiting those opportunitiesthroughmultiple channels [37] Open innovation has becomeincreasingly important for both practice and theory Organi-zations need shorter innovation cycles time and lower RampDcosts

The managerial challenges of open innovation involveutilizing external knowledge then identifying useful externalknowledge and integrating that knowledge into firm Forexample new products have significant trade-offs betweeninnovation speed in new product development costs andcompetitive advantage in relying on external knowledgerather than benefit in current internal knowledge [38] Theopen innovation phenomenon is reinforced by increasingglobalization of research technologies and innovation newinformation and communication technologies and poten-tials of new organizational forms and business models [39]

Chesbrough et al [36] show that the open innovationparadigm treats RampD as an open system in which valuableideas come from inside or outside of company and go tomar-ket from inside or spin-off from the firm (see Figure 1) Thisstudy combines the concepts of open innovation strategicalliance andDeNovo programming approach that integratesIC design service firmrsquos resource into IC semiconductorsindustry

3 Methodology

As seen in the above discussion integrated resources playsa central role in universal industry Specially in IC designservice firmrsquos allocates resource and promotes sustainabilityefforts Next section explores the construction of an efficientplanningmodel for strategy alliance alternative to integratingresources using De Novo programming

31 Integrating Resource Allocation The semiconductorindustry face too many challenge including cost downcompetitor treat human resource constraint and otherresources limitation which in turn affects IC designeffectiveness and business operational performance Optimalhuman resource allocation bymathematic programming thusbecomes a key issue in IC design service firms Mathematicprogramming distributes limited resources to competingactivities to achieve optimal resource allocation Linearprogramming is the most popular mathematic programmingapproach Kantorovich and Koopmans [40] develop linearprogramming formula which can be described as follows

Max Cx

st Ax le b

x ge 0

(1)

Here C = C119902times119899 and A = A119898times119899 are matrices b = (1198871

119887119898)119879isin 119877119898 and x = (119909119894 119909119895 119909119899)

119879isin 119877119899 Let the 119896th

row of119862 be denoted by119862119896 = (119888119896119894 119888119896

119895 119888119896119899) isin 119877119899 and119862119896119909

(119896 = 1 119902) is the 119896th evaluation criterion or alternativeobjective function This linear programming problem canbe solved in several ways which include using the simplexmethod or the interior-point algorithm that allocates lim-ited resource problems Although mathematic programmingoffers a solution method to the resource allocation problemsit is unreasonable to assume additivity when extending thismethod to manage an alliance resourceThis situation is baseon additivity which presumes that all productive elementsare independent and the total effects equal the summationof each individual effect Such assumption does not fit firmrsquosneeds when the firm expects to create a synergies effect ofbusiness operations

The concept of emerging mass customization has beenproposed for solving the problem of element independenceGenerally a company can obtain profits in twoways One is toincrease revenue with higher unit price through customizingand the other is to reduce the cost of unit through max eco-nomic scale of production With element independence it isimpossible to reduce cost of unit and increase revenue simul-taneously Utilizing the concept of mass customization canrelease limited element independence This study assumesthat there exists a market alliance between companies 119860and 119861 with 120587119860 and 120587119861 denoting their respective profits Thegoal of a company is to maximize profits and the feasiblesolutions are within the space surrounded by dotted linesshown in Figure 2 Compromise solutions are typically thebest decision in traditional mathematic programming and

4 Mathematical Problems in Engineering

Licensing

Technology spinoffsInternal

technology

base

External

technology

base

Other firmrsquos

New

Current

R D

Technology insourcing

market

market

market

Figure 1 The open innovation paradigm (sources from [36])

0

Max

Max

Feasible space

120587A A C

B

120587B

(a)

A C

B

(b)

Figure 2 Feasible options obtained using linear programming

they fall into 119860 cap 119861 Options space contained in points 119860119861 and 119862 that include the ideal point 119862 they are unavailableoptions space what caused with utilizing linear programmingadditivity

According to the assumption of additivity combiningalliance resources allows not only 1 + 1 = 2 but also can obtain1 + 1 gt 2 Therefore synergies concept are popular reasonsfor obtaining optima result through resource alliances [41] Inother words traditionalmathematic programming is rationaland available when a firm has resource constraints thatcannot change if it is produced individually [42] Howeverthe traditionalmethod is no longer suitable when redesigningsystems and the optimal solution was adopted throughstrategic alliances with difference firms

This research uses De Novo programming to releaselimited element independence and to solve the problem ofan optimal resource allocation portfolio through resourcealliances to achieve the aspiration goal

32 De Novo Programming for Alliances Approach The DeNovo programming is one of MCDM method that canremodify the systems to achieve an aspiration goal of firmrsquosexpectation By releasing various constraints the De Novoprogramming attempts to break limitations to achieve theoptimal solution Through formation strategic alliance andresource integration the current work extends the De Novoprogramming to obtain an optimal solution [43ndash45] TheDe Novo perspective combines transaction cost theory andresource-based view (RBV) to provide a holistic perspectivefor achieving an aspiration level [46] In this researchfirm seeks strategic allianceresource integration accordingto firmrsquos needs If the minimum alliance cost lies betweendesign cost and human resource cost Semiconductorrsquos firmshould seek strategic allianceresource integrated for ICdesign human resources sharing The IC design service costcan be modified as if allianceintegration cost is less thanindividual firmrsquos cost summary (le sum119873

119894=1)

Mathematical Problems in Engineering 5

The firm should seek allianceintegration with the otherpartner

From the RBV view firms seek resource alliances capa-bilities by allying with a partner to create synergies based onRBV theory The rule of the RBV can also be modified asif allianceintegration benefit is larger than individual firmrsquosprofit summary (ge sum119873

119894=1)

The firm should seek allianceintegration with the otherfirms

Now we involve the cost and RBV theory into the DeNovo programming if the firm alternatives are based on twodifferent resources 119878 and 119879 the rule of resource integratingcan be expressed as follows

if 119864 (119878 cup 119879) minus 119880 (119862119878119879) gt 119864 (119878) + 119864 (119879) minus 119880 (119862119878) minus 119880 (119862119879) (2)

then the firm should seek resource integration with partnerof industry cluster

Also it can express a general formula as follows

if 119864 (1198781 cup 1198782 sdot sdot sdot cup 119878119873) minus 119880 (119862 alliance cost )

ge

119873

sum119894=1

[119864 (119878119894) minus 119880 (119878119894)] 119894 = 1 2 119873(3)

where119864(sdot) is the benefit function119880(sdot) is the cost function119862119878and 119862119879 denote the total design cost in 119878 and 119879 respectivelyand 119862119878119879 denotes the allianceintegration cost between 119878

and 119879 The probability of 119878 and 119879 events seeking strategicallianceresource integration can be demoted respectively as

119901 (119904) = 1 120582119864 (119878 cup 119879) minus 120579119880 (119862119878119879) gt 119864 (119878) minus 119880 (119862119878)

0 120582119864 (119878 cup 119879) minus 120579119880 (119862119878119879) lt 119864 (119878) minus 119880 (119862119878)

(4a)

119901 (119879) =

1 (1 minus 120582) 119864 (119878 cup 119879) minus (1 minus 120579)119880 (119862119878119879)

gt 119864 (119879) minus 119880 (119862119879)

0 (1 minus 120582) 119864 (119878 cup 119879) minus (1 minus 120579)119880 (119862119878119879)

lt 119864 (119879) minus 119880 (119862119879)

(4b)

where 120582 denotes the percentage of increasing alliance benefitin 119878 and 120579 denotes the percentage of reducing alliance costin 119878 Now we can involve De Novo programming into theplanning model of resource integrating that can be expressedas

max 119864 (119878 cup 119879) minus 119880 (119862119878119879)

st 119908119909 le 119861 119909 ge 0(5)

where 119908 = 119901119860 = (1199081 119908119899) isin 119877119899 and 119901 = (1199011 119901119898) isin

119877119898 and 119861 isin 119877 present the unit price of resources and the totalavailable budget Then the knapsack solution is

119909lowast= [0

119861

119862119896 0]

119879

(6)

where

119888119896

119888lowast119896

= max119895

(119888119895

119888lowast119895

) (7)

Max 0

Max

Feasiblespace

120587R

120587AA

C

B

Figure 3 Feasible options obtained using De Novo programming

The optimal solution to (5) is given by (6) and

119887lowast= 119860119909lowast (8)

The final allianceintegration benefit (Ψ(119878lowast)) in 119878 is

Ψ (119878lowast) = 1198941015840119887lowast119880(minus119862119878119879) (9)

where 119894 is the identity column vector According to (9) wecan judge whether or not the firm should seek strategicallianceresource integration by (3) (4a) and (4b) Further-more using De Novo programming we can easily achieveoptimal resource allocation and create synergies betweenalliances The difference between traditional mathematicprogramming andDeNovo programming lies in the ability oftheDeNovoprogramming to redefine its boundaries throughsystem redesign reconfiguration or reshaping [33] Figure 3shows the difference in feasible options space obtainedthrough De Novo programming method

The greatest difference between Figures 2 and 3 is thatthe unavailable solutions before were made available throughDe Novo programming now In other words the idealpoint 119862 is the goal of optimal solution through strategicallianceresource integrating approach

4 Empirical Study Practice

It is commonly believed that integrating IC design humanresources into IC semiconductor supply chain system canenhance operation performance and such integration wouldrequire a complete planning model to ensure effectivenessThe proposed planning model is applied to IC design servicefirms in Taiwan as a empirical case study to examine theplanning model feasibility

41 The Case Study of Taiwanrsquos IC Service Design FirmTaiwanrsquos semiconductors industry is a globalization businessspecializing as the worldrsquos number one of DRAM IC foundrymanufacturer The IC semiconductors industry is a completesupply chain that includes IC design service supplier andcustomer The IC supply chain system developed becamefirms cluster with difference supply chain speciality needs

6 Mathematical Problems in Engineering

IC design service

Supplier Customer

Servicecluster

Suppliercluster

Customercluster

Figure 4 The supply chain of IC semiconductor industry

(see Figure 4) The IC design service is one of semiconductormanufacturing processing before launching into IC foundryThe IC design service firms obtain the order from customerand through project management controls the design sched-ule for customer needs More than 365 IC design servicefirms have been established around Hsinchu Science Parkin Taiwan This IC service design firm cluster situation isvery special in the world Consequently studying Taiwanrsquos ICdesign service firms is a very interesting empirical case

42 The Problems of IC Semiconductor Industry CurrentlyTaiwanrsquos semiconductors face a very slow economics situationof business operation Government release encourage policytry to save the semiconductor industry but these policies wasfail The economics of semiconductor continues going downof economic curve The IC industry external environmentalfactors are multiples and dynamics specializing on theIC design service sector Also the IC design service firmrequires one high performance project management to helpmanage related task Integrating internal human resource andleveraging external resource and adjusting business strategyformatch customer needs of IC designmarket As such firmsshould offer just-in-time mechanism of services for productsdelivery and quick response system for customers A little losson service process could bring the result of lose customerTherefore IC design firm should determine to operate atlowest cost and high quality level for IC design service Highperformance IC design service systems should be consideredin the semiconductor supply chain system

43 IC Design Service Firms The IC design skill is one of keytechnical of IC semiconductor manufacturing system It is

a new business model emerging as a vertical disintegrationproduct in the IC semiconductor industry The IC semicon-ductor combines multiprocesses into one complete systemincluding IC design and an IDM (integrated device manu-facturer) fabrication assembly test in the 1970s IC-designsand IDM were separated in the 1980s from one complete setsystem to develop two subsystems of IC design and IDMfabrication and continues developing including IC designand IDM fabrication and assembly The IC semiconductorindustry after the 1980s modified processing to include thesystem IC design IDM fabrication IC foundry IC assemblyand IC test on the subsystem The IC design subsystem after2001 extends from one to three parts including SiP (systemin package) design system and IC design The IC designservice firm has become an important process of the ICsemiconductors industry Figure 5 shows IC design servicedevelopment trajectory from 1960 to 2013

The IC design service firm is a high technology knowl-edge industry The IC design service firm requires pro-fessional human design who must have engineering back-ground specialized design technology knowledge and alsoneed high speed internet networks a convenient communi-cation digital platform and a high operation performanceThis study expects a building of an MCDM model with DeNovo programming for integrating the resource of IC designservice firmsThe IC design service firm is multidimensionaland highly competitive focusing on special professionaltechniques and quick response of design services De Novoprogramming helps to plan service model for achievingaspiration levels of the IC design service firms

44 The Relationship of IC Design Service Supplier andCustomer The IC semiconductors industry is a multipleproduction process that includes design fabrication foundrytesting assembly and delivery of products to customer TheIC design service is a substructure under the IC supplierproviding IC design service for supplier or customer The ICdesign service firms offer system in package (SiP) design todirect customer or IC design and IC layout design for supplier(see Figure 6) The IC design service firms offers value addedat each design service process for example highlightingperformance powerful function low production cost systemon chip (SoC) and redownsizing IC design space Figure 6shows relationship between of IC design service supplier andcustomer

The IC design service firms not only provides designservice for IC semiconductors manufacturing systems butalso offers new innovative ideasfunction on IC chip forcustomer The IC design service firms generally providesprofessional IC design knowledge and IC foundry techniquesthat can obtain greater economics efficiency of semiconduc-tors production the result should be increased IC design ser-vice business opportunity Consequently adopting innovativeideas becomes a very important task in IC design serviceprocessing

45 Through De Novo Programming Achieving AspirationGoal This study expects through De Novo programming to

Mathematical Problems in Engineering 7

After 20001980s to 1990sBefore 1970s

IC design

System IC-ASSP IC-ASIC SOC-IP

SystemSystemSystemSystemSystemIC designIDM fabassembly

test

DS

IC design

IDM fab

Foundry

Assembly

Test

IC design

IDM fab

Foundry

Assembly

Test

IC design

IDM fab

Assembly

SystemIC designIDM fabassembly

test

20001990198019701960 2013

SIP

Figure 5 The IC design service development trajectory from 1960 to 2013 year

IC designservice

Supplier CustomerFinished products

SiP design service

Design requirement IC design service

Order requirement

IC layout order

Figure 6 The relationship of IC design service supplier andcustomer

achieve optimal resource integrating purpose The IC designservice firm must consider resource constrain under mul-tiple objectives alternatives before making decisions Firmface multiple objectives and evaluation criteria of problemnamely dealing with resources integrating and adjustingbusiness strategic to achieve the aspiration goal of businessIn general through trade-off skill it is almost impossible toobtain optimization of all criteria of a given system Zeleny[32] states the new system can eliminate the resource con-strain through redesign then preferably obtain optimizationresults what can improves theTrade-off problems Zeleny [35]proposes the optimal portfolio resource allocation conceptwhich designs a resource integrated system that is individualresource levels cannot determine separately so new designsystems do not have trade-offs to consider Zeleny [33]develops a De Novo programming method for designing an

Low

High

High

For achieving the aspireddesired

level

Good

The best

The best

Efficiencies

Perfo

rman

ce

Figure 7Through De Novo programming achieving the aspirationgoal

optimal system by reshaping the feasible set [33] Firm canmake strategy alliances with partner of supply chain systemand achieves resource integration adjusting the businessgoal remodifying the business model and justifying targetcustomer services This study expects through De Novo pro-gramming approach to compute the data of resource integrat-ing and justify the resource allocationobjective alternativefrom a good level to best level thus achieving aspiration goal(see Figure 7)

8 Mathematical Problems in Engineering

46 Empirical Case Study

461 A Case Study of IC Production Firm A general case ofIC production problem involving two IC products GPS andcell phone of IC in quantities 1199091 and 1199092 each consuming fivedifferent resources (unitmarket prices of resources are given)The data is summarized as shown in Table 1

The costs of the given resources portfolio

(28 times 32) + (35 times 30) + (10 times 55)

+ (18 times 10) + (10 times 28) = $2956(10)

Unit costs of producing one unit goods of the two products

1199091 = (28 lowast 6) + (35 lowast 3) + (10 lowast 14) + (18 lowast 2)

+ (10 lowast 6) = $509

1199092 = (28 lowast 2) + (35 lowast 5) + (10 lowast 6) + (18 lowast 5)

+ (10 lowast 8) = $461

(11)

Expected profit margins (price-cost) arethe profit of 1199091 product = $549 minus $509 = $40unitthe profit of 1199092 product = $491 minus $461 = $30unit

Maximizing total value of function 11989111198911 = 401199091 + 301199092 (12)

Maximizing total quality index 11989121198912 = 81199091 + 101199092 (13)

Maximizing levels of two products calculated by De Novoprogramming

max 1198911 = 401199091 + 301199092

max 1198912 = 81199091 + 101199092

st 61199091 + 21199092 le 32

31199091 + 51199092 le 30

141199091 + 61199092 le 55

21199091 + 51199092 le 10

61199091 + 81199092 le 28

1199091 1199092 ge 0

(14)

Maximum 1198911 in profit

max 1198911 997888rarr 1199091 = 371

1199092 = 052

119891lowast

1= 40 times 371 + 30 times 052 = $164

(15)

Maximum 1198912 in total quality index

max 1198912 997888rarr 1199091 = 371

1199092 = 052

119891lowast

2= 8 times 371 + 10 times 052 = $3488

(16)

Minimizing the total cost by considering the followingconstraints

min 5091199091 + 4611199092

st 401199091 + 301199092 le 164

81199091 + 101199092 le 3488

(17)

Maximum 1198911 in profit

max 1198911 997888rarr 1199091 = 371

1199092 = 052

119891lowast

1= 40 times 371 + 30 times 052 = $164

(18)

Maximum 1198912 in total quality index

max 1198912 997888rarr 1199091 = 371

1199092 = 052

119891lowast

2= 8 times 371 + 10 times 052 = $3488

(19)

Cost of the newly designed system

(28 times 2328) + (35 times 1368) + (10 times 5506)

+ (18 times 1002) + (10 times 2642) = $21258(20)

The new portfolio of resources proposed by the consultant isshown in Table 2

The result of data analysis shows that the new planningsystem cost is $21258

The given resource portfolio cost is $2956 Comparisonbetween original and new planning model we found outthat the new planning model can reduce cost to $8305 andthat new resource planning model is one reliable modelConsequently the IC design service firm can follow the newplanningmodel ofDe novo programming to process resourceintegration

462 The Case of IC Design Service Firm The IC designservice firm is a professional workshop that focuses on theIC design task dependent on what design they can provideservice The IC design service task is also a project-basedplanning under a large RampDproject with business developingstrategy A project-based firm uses external delivery projectsfor business purposes [47 48] This study utilizes a real caseof IC design project management to practice on IC designservice firm Project planning may include many subprojectsthat depend on different cases of customerrsquos demand Centralfeatures of project management were identified according toindividual project uniqueness human resources of projectand business network complexity discontinuity of demandand relationships between other projects and considerablefinancial commitment of the parties [49 50] Conductingaspiration project planning under resource constraint is atarget issue for the IC design service firm This real caseincludes five different resources which included humanpower resource design equipments design materials special

Mathematical Problems in Engineering 9

Table 1 The IC of GPS and cell phone production requiring material

Unit price ($) Material of production processTechnological coefficients

Resource portfolio(units)(Resource requirement)

1199091

1199092

28 Silver wire 6 2 3235 Golden wire 3 5 3010 Silicon Crystal 14 6 5518 Chemistry material 2 5 1010 Conductor material 6 8 28

Table 2 The new resource portfolio planning of IC of GPS and cell phone

Unit price ($) Material of production processTechnological coefficients

Resource portfolio(units)(Resource requirement)

1199091

1199092

28 Silver wire 6 2 232835 Golden wire 3 5 136810 Silicon Crystal 14 6 550618 Chemistry material 2 5 100210 Conductor material 6 8 2642

technical human and design outsourcing with two differentproject teams for the A company customer to performthe IC design task The current study names the twoprojects as the 1198751 and 1198752 project team programs This realplanning program case utilizes the De Novo programmingfor adjustable resource allocation and seeks out minimumcost with maximum performance This work transitions thefive kinds of resources (design equipments design humanpower design material special technical human and design-outsourcing) in the case study to dollar amounts for easy costcalculation Design equipments cost is $380 per unit designhuman power labor cost is $240 per unit design materialresource cost is $200 special technical human cost is $260per unit and design outsourcing cost is $120 per unit

Costs of given resources portfolio

(380 times 26) + (240 times 50) + (200 times 30) + (120 times 10)

+ (260 times 16) = $33240(21)

Unit costs for running each project of the two projects

1198751 = (380 lowast 8) + (240 lowast 10) + (200 lowast 4)

+ (260 lowast 6) = $7800

1198752 = (380 lowast 6) + (240 lowast 4) + (200 lowast 4)

+ (120 lowast 4) = $4520

(22)

Expected profit margins (price cost) are

1198751 = $8200 minus $7800 = $400unit

1198752 = $4820 minus $4520 = $300unit(23)

Maximizing total value of function 1198911

1198911 = 4001199011 + 3001199012 (24)

Maximizing total quality index 1198912

1198912 = 81199011 + 101199012 (25)

Maximizing levels of the two projects calculated by De Novoprogramming

max 1198911 = 4001199011 + 3001199012

max 1198912 = 81199011 + 101199012

st 81199011 + 61199012 le 26

101199011 + 41199012 le 50

41199011 + 41199012 le 30

41199012 le 10

61199011 le 16

1199011 1199012 ge 0

(26)

Maximum 1198911 in profit

max 1198911 997888rarr 1199011 = 267

1199012 = 078

119891lowast

1= 400 times 267 + 300 times 078 = $1300

(27)

10 Mathematical Problems in Engineering

Table 3 1198751and 119875

2project team program require resource of IC design service

Unit price ($) Resources of IC designProject coefficients

Resource portfolio(units)

(Resource requirement)1198751

1198752

380 Design equipments 8 6 26240 Human power resource 10 4 50200 Design material 4 4 30120 Design outsource 0 4 10260 Special technical human 6 0 16

Table 4 The new resource portfolio planning model of 1198751and 119875

2project team program

Unit price ($) Resources of IC designProject coefficients

Resource portfolio(units)(Resource requirement)

1198751

1198752

380 Design equipments 8 6 26240 Human power resource 10 4 325200 Design material 4 4 13120 Design outsource 0 4 0260 Special technical human 6 0 195

Maximum 1198912 in total quality index

max 1198912 997888rarr 1199011 = 267

1199012 = 078

119891lowast

2= 8 times 267 + 10 times 078 = $2916

(28)

Minimizing the total cost by considering the followingconstraints

min 78001199011 + 45201199012

st 1198911 = 4001199011 + 3001199012 le 1300

1198912 = 81199011 + 101199012 le 2916

(29)

Maximum 1198911 in profit

max 1198911 997888rarr 1199011 = 325

1199012 = 0

119891lowast

1= 400 times 325 = $1300

(30)

Maximum 1198912 in total quality index

max 1198912 997888rarr 1199011 = 325

1199012 = 0

119891lowast

2= 8 times 325 = $26

(31)

Cost of new design planning

(380 times 26) + (240 times 325) + (200 times 13) + (260 times 195)

= $25350(32)

New portfolio of resources proposed by the consultant isshown in Table 4

47 Discussions

471 The General Case of IC Production Firm The datashow the empirical case study using the De Novo program-ming which remodifies the planning model and adjusts theresources allocation portfolio The new plan model offersthe best optimized resource integration and reduces losingand enhances firmrsquos competitiveness Technical problemsprogramming are discussed as follows

(i) The original plan needs a total cost of $2956 withgiven resources portfolios

[(28 times 32) + (35 times 30) + (10 times 55) + (18 times 10)

+ (10 times 28) = $2956] (33)

(ii) The new planning model of resource allocation onlyrequires cost of $21258

[(28 times 2328) + (35 times 1368) + (10 times 5506)

+ (18 times 1002) + (10 times 2642) = $21258] (34)

(iii) The newly planning model can save $8302 costConsequently firm seeks new planning model tointegrate resource

$2956 minus $21258 = $8302 (35)

(iv) The newly planning model use the De Novo pro-gramming to reduce resource loss and get betterperformance than the original planning model Thispaper also utilizes the empirical case examination ofDe Novo programming for IC design service firmperformance The new planning model obtains muchbetter resource integration

Mathematical Problems in Engineering 11

472 The Case of IC Design Service Firm Firms have massproduction capacity limitation per one time period due tolimited resources especially for the preceding competitivefacility location problem on the decentralized supply chain[51] The IC design service firm is a standard project-basedprogrammodel Many project teams program through coop-eration under a large design program for different customerare needed This study utilizes a case study of IC design firmfor planning an IC design to customer serviceThis case studyincludes two project teams The 1198751 and 1198752 project with twodifferent plan programming on IC design see (Table 3) Oneproject team leader prefers hiring a special technical engineerfor the IC design task that can maintain the IC design keytechnician and train other staffThe other project team leaderprefers outsourcing design that reduces cost The IC designservice firm chooses the alternative optimal project teambased on the result of De Novo programming

The resource portfolio of firmrsquos original cost is as follows

(380 times 26) + (240 times 50) + (200 times 30) + (120 times 10)

+ (260 times 16) = $33240(36)

The cost of newly planning model through the De Novoprogramming is as follows

(380 times 26) + (240 times 325) + (200 times 13) + (260 times 195)

= $25350(37)

The new planning model saves cost as follows

$33240 minus $25350 = $7890 (38)

The IC design firm adopting1198751 project team cost is as follows

1199011 (8 times 26) + (10 times 325) + (4 times 13) + (6 times 195) = $702(39)

The IC design firm adopting1198752 project team cost is as follows

1199012 (6 times 26) + (4 times 325) + (4 times 13) = $338 (40)

The result of data analysis shows through the De Novoprogramming method the IC design service firm that canfind out which project team is the optimal solution In thisempirical case adopting 1198752 project team can save design costmuchmore than1198751 projectThe new planningmodel reduces$7890 in cost compared with the original planning modelSo the newly planning model is the best choice for achievingaspiration goal

5 Conclusion

The IC semiconductor industry faces uncertainty anddynamic external environment especially at IC design servicefirm that needs one resource integrating programmingmodelfor new product development [52] Adopting an efficientstrategic alliancesresource integrating planning model is akey issue for achieving firmrsquos business goals especially astheir resource was constrainedThe present study considers a

planningmodel based on resource constraintsThis study uti-lizes the De Novo programming for achieving firmrsquos strategicgoal The De Novo programming can help firms find out anoptimal resource integration base on firm financial considerThis planning model provided reliable systematize approachthat combines alliances from external resources and changethem to internal resources application The project team1198752 adopting open innovation concept through outsourcingreduces design cost and enhances design performance DeNovo programming not only obtains optimal resource inte-gration but also promotes the conceptual of strategic alliancesfor firmrsquos cooperation between each other

51 Management Implication This research aims to create aresource integrating plan model through IC design servicefirm as an empirical case for examining the result of theplanning model The planning model is based on openinnovation theory exploring the IC design service firmwhichintegrates IC design resources and makes optimal resourcesintegrate in IC design service firm The planning model ofDe Novo programming is not only for IC design service firmbut also can apply to the other industrial implementationstrategic allianceresource integration This planning modelis a universal model for the other industry fields

52 Suggestion The IC design service industry is a veryspecial industry We suggest the extend to the other industrythat can find out different results Suggestion utilizes the otherapproach of MCDMmethod exploring different case studiesof industry

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

The authors appreciate two blind reviewers who have pro-vided very excellent comments These comments are verygood for the authors to improve the quality of the paperAlso thanks to the editor Professor Chang for offering lots ofexcellent suggestions that helped enhance this paperrsquos quality

References

[1] R Varadarajan ldquoOutsourcing think more expansivelyrdquo Journalof Business Research vol 62 no 11 pp 1165ndash1172 2009

[2] G Forgionne and Z Guo ldquoInternal supply chain coordinationin the electric utility industryrdquo European Journal of OperationalResearch vol 196 no 2 pp 619ndash627 2009

[3] R George and R Kabir ldquoBusiness groups and profit redistribu-tion a boon or bane for firmsrdquo Journal of Business Research vol61 no 9 pp 1004ndash1014 2008

[4] L Duckstein and S Opricovic ldquoMultiobjective optimization inriver basin developmentrdquoWater Resources Research vol 16 no1 pp 14ndash20 1980

12 Mathematical Problems in Engineering

[5] M Zeleny ldquoA case study in multiple objective design DeNovo programmingrdquo inMultiple Criteria Analysis OperationalMethods vol 1 pp 37ndash52 1981

[6] M Zeleny ldquoOptimal system design with multiple criteria DeNovo programming approachrdquo Engineering Costs and Produc-tion Economics vol 10 no 1 pp 89ndash94 1986

[7] S Christophersona M Kitsonb and J Michiec ldquoInnovationnetworks and knowledge exchangerdquo Cambridge Journal ofRegions Economy and Society vol 1 no 2 pp 165ndash173 2008

[8] H W Chesbrough Open Innovation The New Imperativefor Creating and Profiting from Technology Harvard BusinessSchool Press Boston Mass USA 2003

[9] J B Barney ldquoFirm resources and sustained competitive advan-tagerdquo Advances in Strategic Management vol 17 pp 203ndash2272000

[10] B Wernerfelt ldquoA resource-based view of the firmrdquo StrategicManagement Journal vol 5 no 2 pp 171ndash180 1984

[11] T R Holcomb and M A Hitt ldquoToward a model of strategicoutsourcingrdquo Journal of Operations Management vol 25 no 2pp 464ndash481 2007

[12] D J Collis ldquoA resource-based analysis of global competitionthe case of the bearings industryrdquo Strategic Management Jour-nal vol 12 no 1 pp 49ndash68 1991

[13] E Penrose The Theory of the Growth of the Firm John Wileyand Sons New York NY USA 1959

[14] E Anderson andH Gatignon ldquoModes of foreign entry a trans-action cost analysis and propositionsrdquo Journal of InternationalBusiness Studies vol 17 no 3 pp 1ndash26 1986

[15] J Chimhanzi and R E Morgan ldquoExplanations from themarketinghuman resources dyad formarketing strategy imple-mentation effectiveness in service firmsrdquo Journal of BusinessResearch vol 58 no 6 pp 787ndash796 2005

[16] J Fahy ldquoA resource-based analysis of sustainable competitiveadvantage in a global environmentrdquo International BusinessReview vol 11 no 1 pp 57ndash78 2002

[17] M A Harrison R E Hoskisson and D Ireland ldquoSynergies andpost acquisition performance differences versus similarities inresource allocationsrdquo Journal of Management vol 17 no 1 pp173ndash190 1991

[18] D J Teece G Pisano and A Shuen ldquoDynamic capabilities andstrategic managementrdquo Strategic Management Journal vol 18no 7 pp 509ndash533 1997

[19] V A Varma J F Pekny G E Blau and G V Reklaitis ldquoAframework for addressing stochastic and combinatorial aspectsof scheduling and resource allocation in pharmaceutical RampDpipelinesrdquoComputers andChemical Engineering vol 32 no 4-5pp 1000ndash1015 2008

[20] R Kolisch ldquoEfficient priority rules for the resource-constrainedproject scheduling problemrdquo Journal of Operations Manage-ment vol 14 no 3 pp 179ndash192 1996

[21] R Kolisch and S Hartmann ldquoExperimental investigationof heuristics for resource-constrained project scheduling anupdaterdquo European Journal of Operational Research vol 174 no1 pp 23ndash37 2006

[22] M J Leiblein ldquoThe choice of organizational governance formand performance predictions from transaction cost resource-based and real options theoriesrdquo Journal of Management vol29 no 6 pp 937ndash961 2003

[23] G CMoore and I Benbasat ldquoDevelopment of an instrument tomeasure the perceptions of adopting an information technologyinnovationrdquo Information Systems Research vol 2 no 3 pp 192ndash222 1991

[24] R E Hoskisson M A Hitt W P Wan and D Yiu ldquoTheoryand research in strategic management swings of a pendulumrdquoJournal of Management vol 25 no 3 pp 417ndash456 1999

[25] C C Miller and L B Cardinal ldquoStrategic planning and firmperformance a synthesis of more than two decades of researchrdquoAcademy of Management Journal vol 37 no 6 pp 1649ndash16651994

[26] S D Hunt A General Theory of Competition Sage ThousandOaks Calif USA 2000

[27] S Hart and C Banbury ldquoHow strategy-making processes canmake a differencerdquo Strategic Management Journal vol 15 no 4pp 251ndash269 1994

[28] E Drakopoulos ldquoEnterprise network planning and designmethodology and applicationrdquo Computer Communications vol22 no 4 pp 340ndash352 1999

[29] V Kumar B Maheshwari and U Kumar ldquoERP systems imple-mentation best practices in Canadian government organiza-tionsrdquoGovernment InformationQuarterly vol 19 no 2 pp 147ndash172 2002

[30] W R Lewis ldquoStrategic planningrdquo Hospital Materiel Manage-ment Quarterly vol 10 no 4 pp 57ndash63 1989

[31] D Wainwright and T Waring ldquoThree domains for imple-menting integrated information systems redressing the bal-ance between technology strategic and organisational analysisrdquoInternational Journal of InformationManagement vol 24 no 4pp 329ndash346 2004

[32] M Zeleny ldquoOptimal system design with multiple criteria DeNovo programming approachrdquo Engineering Costs and Produc-tion Economics vol 10 no 1 pp 89ndash95 1986

[33] M Zeleny ldquoOptimal given system versus designing optimalsystem the De Novo programming approachrdquo InternationalJournal of General System vol 17 no 3 pp 295ndash307 1990

[34] Y M Zhang G H Huang and X D Zhang ldquoInexact de Novoprogramming for water resources systems planningrdquo EuropeanJournal ofOperational Research vol 199 no 2 pp 531ndash541 2009

[35] M Zeleny ldquoTrade-offs free management via De Novo pro-grammingrdquo International Journal Operations and QuantitativeManagement vol 1 no 1 pp 3ndash13 1995

[36] H Chesbrough W Anhaverbeke and J West Eds Openinnovation Researching a New Paradigm Oxford UniversityPress 2006

[37] J West and S Gallagher ldquoChallenges of open innovation theparadox of firm investment in open-source softwarerdquo R amp DManagement vol 36 no 3 pp 319ndash331 2006

[38] E H Kessler P E Bierly and S Gopalakrishnan ldquoInternalversus external learning in new product development effects onspeed costs and competitive advantagerdquo R amp D Managementvol 30 no 3 pp 213ndash223 2000

[39] O Gassmann and E Enkel ldquoTowards a theory of open innova-tion three core process archetypesrdquo in Proceedings of the RampDManagement Conference (RADMA rsquo04) Lisbon Portugal 2004

[40] L V Kantorovich and T C Koopmans Problems of Applicationof Optimization Methods in Industry Federation of SwedishIndustry 1976

[41] T K Das and B-S Teng ldquoA resource-based theory of strategicalliancesrdquo Journal of Management vol 26 no 1 pp 31ndash61 2000

[42] Z Babic and I Pavic ldquoMulticriterial production planning byDe Novo programming approachrdquo International Journal ofProduction Economics vol 43 no 1 pp 59ndash66 1996

[43] J K C Chen and G H Tzeng ldquoPerspective strategic allianceand resource allocation in supply chain systems through the De

Mathematical Problems in Engineering 13

Novo programming approachrdquo International Journal Sustain-able Strategic Management vol 1 no 3 pp 320ndash339 2009

[44] J-J Huang G-H Tzeng and C-S Ong ldquoMotivation andresource-allocation for strategic alliances through the De NovoperspectiverdquoMathematical and ComputerModelling vol 41 no6-7 pp 711ndash721 2005

[45] J-J Huang G-H Tzeng and C-S Ong ldquoChoosing bestalliance partners and allocating optimal alliance resources usingthe fuzzymulti-objective dummyprogrammingmodelrdquo Journalof the Operational Research Society vol 57 no 10 pp 1216ndash12232006

[46] R M Grant ldquoThe resource-based theory competitive advan-tage implications for strategy formulationrdquo California Manage-ment Review vol 33 no 3 pp 114ndash135 1991

[47] K A Artto and K Wikstrom ldquoWhat is project businessrdquoInternational Journal of Project Management vol 23 no 5 pp343ndash353 2005

[48] J Soderlund ldquoOn the broadening scope of the research onprojects a review and a model for analysisrdquo InternationalJournal of ProjectManagement vol 22 no 8 pp 655ndash667 2004

[49] B Cova P Ghauri and R Salle Project Marketing BeyondCompetitive Bidding JohnWiley amp Sons Chichester UK 2002

[50] T Mandjak and Z Veres ldquoThe D-U-C model and the stages ofthe project marketing processrdquo in Proceedings of the 14th IMPAnnual Conference Proceedings H K Nummela Ed pp 471ndash490 1998

[51] Q Meng Y Huang and R L Cheu ldquoCompetitive facilitylocation on decentralized supply chainsrdquo European Journal ofOperational Research vol 196 no 2 pp 487ndash499 2009

[52] H-M Hsu and W-P Wang ldquoDynamic programming fordelayed product differentiationrdquo European Journal of Opera-tional Research vol 156 no 1 pp 183ndash193 2004

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Mathematical Problems in Engineering

Hindawi Publishing Corporationhttpwwwhindawicom

Differential EquationsInternational Journal of

Volume 2014

Applied MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Mathematical PhysicsAdvances in

Complex AnalysisJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

OptimizationJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Operations ResearchAdvances in

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Function Spaces

Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of Mathematics and Mathematical Sciences

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

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Algebra

Discrete Dynamics in Nature and Society

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

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Decision SciencesAdvances in

Discrete MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom

Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Stochastic AnalysisInternational Journal of

4 Mathematical Problems in Engineering

Licensing

Technology spinoffsInternal

technology

base

External

technology

base

Other firmrsquos

New

Current

R D

Technology insourcing

market

market

market

Figure 1 The open innovation paradigm (sources from [36])

0

Max

Max

Feasible space

120587A A C

B

120587B

(a)

A C

B

(b)

Figure 2 Feasible options obtained using linear programming

they fall into 119860 cap 119861 Options space contained in points 119860119861 and 119862 that include the ideal point 119862 they are unavailableoptions space what caused with utilizing linear programmingadditivity

According to the assumption of additivity combiningalliance resources allows not only 1 + 1 = 2 but also can obtain1 + 1 gt 2 Therefore synergies concept are popular reasonsfor obtaining optima result through resource alliances [41] Inother words traditionalmathematic programming is rationaland available when a firm has resource constraints thatcannot change if it is produced individually [42] Howeverthe traditionalmethod is no longer suitable when redesigningsystems and the optimal solution was adopted throughstrategic alliances with difference firms

This research uses De Novo programming to releaselimited element independence and to solve the problem ofan optimal resource allocation portfolio through resourcealliances to achieve the aspiration goal

32 De Novo Programming for Alliances Approach The DeNovo programming is one of MCDM method that canremodify the systems to achieve an aspiration goal of firmrsquosexpectation By releasing various constraints the De Novoprogramming attempts to break limitations to achieve theoptimal solution Through formation strategic alliance andresource integration the current work extends the De Novoprogramming to obtain an optimal solution [43ndash45] TheDe Novo perspective combines transaction cost theory andresource-based view (RBV) to provide a holistic perspectivefor achieving an aspiration level [46] In this researchfirm seeks strategic allianceresource integration accordingto firmrsquos needs If the minimum alliance cost lies betweendesign cost and human resource cost Semiconductorrsquos firmshould seek strategic allianceresource integrated for ICdesign human resources sharing The IC design service costcan be modified as if allianceintegration cost is less thanindividual firmrsquos cost summary (le sum119873

119894=1)

Mathematical Problems in Engineering 5

The firm should seek allianceintegration with the otherpartner

From the RBV view firms seek resource alliances capa-bilities by allying with a partner to create synergies based onRBV theory The rule of the RBV can also be modified asif allianceintegration benefit is larger than individual firmrsquosprofit summary (ge sum119873

119894=1)

The firm should seek allianceintegration with the otherfirms

Now we involve the cost and RBV theory into the DeNovo programming if the firm alternatives are based on twodifferent resources 119878 and 119879 the rule of resource integratingcan be expressed as follows

if 119864 (119878 cup 119879) minus 119880 (119862119878119879) gt 119864 (119878) + 119864 (119879) minus 119880 (119862119878) minus 119880 (119862119879) (2)

then the firm should seek resource integration with partnerof industry cluster

Also it can express a general formula as follows

if 119864 (1198781 cup 1198782 sdot sdot sdot cup 119878119873) minus 119880 (119862 alliance cost )

ge

119873

sum119894=1

[119864 (119878119894) minus 119880 (119878119894)] 119894 = 1 2 119873(3)

where119864(sdot) is the benefit function119880(sdot) is the cost function119862119878and 119862119879 denote the total design cost in 119878 and 119879 respectivelyand 119862119878119879 denotes the allianceintegration cost between 119878

and 119879 The probability of 119878 and 119879 events seeking strategicallianceresource integration can be demoted respectively as

119901 (119904) = 1 120582119864 (119878 cup 119879) minus 120579119880 (119862119878119879) gt 119864 (119878) minus 119880 (119862119878)

0 120582119864 (119878 cup 119879) minus 120579119880 (119862119878119879) lt 119864 (119878) minus 119880 (119862119878)

(4a)

119901 (119879) =

1 (1 minus 120582) 119864 (119878 cup 119879) minus (1 minus 120579)119880 (119862119878119879)

gt 119864 (119879) minus 119880 (119862119879)

0 (1 minus 120582) 119864 (119878 cup 119879) minus (1 minus 120579)119880 (119862119878119879)

lt 119864 (119879) minus 119880 (119862119879)

(4b)

where 120582 denotes the percentage of increasing alliance benefitin 119878 and 120579 denotes the percentage of reducing alliance costin 119878 Now we can involve De Novo programming into theplanning model of resource integrating that can be expressedas

max 119864 (119878 cup 119879) minus 119880 (119862119878119879)

st 119908119909 le 119861 119909 ge 0(5)

where 119908 = 119901119860 = (1199081 119908119899) isin 119877119899 and 119901 = (1199011 119901119898) isin

119877119898 and 119861 isin 119877 present the unit price of resources and the totalavailable budget Then the knapsack solution is

119909lowast= [0

119861

119862119896 0]

119879

(6)

where

119888119896

119888lowast119896

= max119895

(119888119895

119888lowast119895

) (7)

Max 0

Max

Feasiblespace

120587R

120587AA

C

B

Figure 3 Feasible options obtained using De Novo programming

The optimal solution to (5) is given by (6) and

119887lowast= 119860119909lowast (8)

The final allianceintegration benefit (Ψ(119878lowast)) in 119878 is

Ψ (119878lowast) = 1198941015840119887lowast119880(minus119862119878119879) (9)

where 119894 is the identity column vector According to (9) wecan judge whether or not the firm should seek strategicallianceresource integration by (3) (4a) and (4b) Further-more using De Novo programming we can easily achieveoptimal resource allocation and create synergies betweenalliances The difference between traditional mathematicprogramming andDeNovo programming lies in the ability oftheDeNovoprogramming to redefine its boundaries throughsystem redesign reconfiguration or reshaping [33] Figure 3shows the difference in feasible options space obtainedthrough De Novo programming method

The greatest difference between Figures 2 and 3 is thatthe unavailable solutions before were made available throughDe Novo programming now In other words the idealpoint 119862 is the goal of optimal solution through strategicallianceresource integrating approach

4 Empirical Study Practice

It is commonly believed that integrating IC design humanresources into IC semiconductor supply chain system canenhance operation performance and such integration wouldrequire a complete planning model to ensure effectivenessThe proposed planning model is applied to IC design servicefirms in Taiwan as a empirical case study to examine theplanning model feasibility

41 The Case Study of Taiwanrsquos IC Service Design FirmTaiwanrsquos semiconductors industry is a globalization businessspecializing as the worldrsquos number one of DRAM IC foundrymanufacturer The IC semiconductors industry is a completesupply chain that includes IC design service supplier andcustomer The IC supply chain system developed becamefirms cluster with difference supply chain speciality needs

6 Mathematical Problems in Engineering

IC design service

Supplier Customer

Servicecluster

Suppliercluster

Customercluster

Figure 4 The supply chain of IC semiconductor industry

(see Figure 4) The IC design service is one of semiconductormanufacturing processing before launching into IC foundryThe IC design service firms obtain the order from customerand through project management controls the design sched-ule for customer needs More than 365 IC design servicefirms have been established around Hsinchu Science Parkin Taiwan This IC service design firm cluster situation isvery special in the world Consequently studying Taiwanrsquos ICdesign service firms is a very interesting empirical case

42 The Problems of IC Semiconductor Industry CurrentlyTaiwanrsquos semiconductors face a very slow economics situationof business operation Government release encourage policytry to save the semiconductor industry but these policies wasfail The economics of semiconductor continues going downof economic curve The IC industry external environmentalfactors are multiples and dynamics specializing on theIC design service sector Also the IC design service firmrequires one high performance project management to helpmanage related task Integrating internal human resource andleveraging external resource and adjusting business strategyformatch customer needs of IC designmarket As such firmsshould offer just-in-time mechanism of services for productsdelivery and quick response system for customers A little losson service process could bring the result of lose customerTherefore IC design firm should determine to operate atlowest cost and high quality level for IC design service Highperformance IC design service systems should be consideredin the semiconductor supply chain system

43 IC Design Service Firms The IC design skill is one of keytechnical of IC semiconductor manufacturing system It is

a new business model emerging as a vertical disintegrationproduct in the IC semiconductor industry The IC semicon-ductor combines multiprocesses into one complete systemincluding IC design and an IDM (integrated device manu-facturer) fabrication assembly test in the 1970s IC-designsand IDM were separated in the 1980s from one complete setsystem to develop two subsystems of IC design and IDMfabrication and continues developing including IC designand IDM fabrication and assembly The IC semiconductorindustry after the 1980s modified processing to include thesystem IC design IDM fabrication IC foundry IC assemblyand IC test on the subsystem The IC design subsystem after2001 extends from one to three parts including SiP (systemin package) design system and IC design The IC designservice firm has become an important process of the ICsemiconductors industry Figure 5 shows IC design servicedevelopment trajectory from 1960 to 2013

The IC design service firm is a high technology knowl-edge industry The IC design service firm requires pro-fessional human design who must have engineering back-ground specialized design technology knowledge and alsoneed high speed internet networks a convenient communi-cation digital platform and a high operation performanceThis study expects a building of an MCDM model with DeNovo programming for integrating the resource of IC designservice firmsThe IC design service firm is multidimensionaland highly competitive focusing on special professionaltechniques and quick response of design services De Novoprogramming helps to plan service model for achievingaspiration levels of the IC design service firms

44 The Relationship of IC Design Service Supplier andCustomer The IC semiconductors industry is a multipleproduction process that includes design fabrication foundrytesting assembly and delivery of products to customer TheIC design service is a substructure under the IC supplierproviding IC design service for supplier or customer The ICdesign service firms offer system in package (SiP) design todirect customer or IC design and IC layout design for supplier(see Figure 6) The IC design service firms offers value addedat each design service process for example highlightingperformance powerful function low production cost systemon chip (SoC) and redownsizing IC design space Figure 6shows relationship between of IC design service supplier andcustomer

The IC design service firms not only provides designservice for IC semiconductors manufacturing systems butalso offers new innovative ideasfunction on IC chip forcustomer The IC design service firms generally providesprofessional IC design knowledge and IC foundry techniquesthat can obtain greater economics efficiency of semiconduc-tors production the result should be increased IC design ser-vice business opportunity Consequently adopting innovativeideas becomes a very important task in IC design serviceprocessing

45 Through De Novo Programming Achieving AspirationGoal This study expects through De Novo programming to

Mathematical Problems in Engineering 7

After 20001980s to 1990sBefore 1970s

IC design

System IC-ASSP IC-ASIC SOC-IP

SystemSystemSystemSystemSystemIC designIDM fabassembly

test

DS

IC design

IDM fab

Foundry

Assembly

Test

IC design

IDM fab

Foundry

Assembly

Test

IC design

IDM fab

Assembly

SystemIC designIDM fabassembly

test

20001990198019701960 2013

SIP

Figure 5 The IC design service development trajectory from 1960 to 2013 year

IC designservice

Supplier CustomerFinished products

SiP design service

Design requirement IC design service

Order requirement

IC layout order

Figure 6 The relationship of IC design service supplier andcustomer

achieve optimal resource integrating purpose The IC designservice firm must consider resource constrain under mul-tiple objectives alternatives before making decisions Firmface multiple objectives and evaluation criteria of problemnamely dealing with resources integrating and adjustingbusiness strategic to achieve the aspiration goal of businessIn general through trade-off skill it is almost impossible toobtain optimization of all criteria of a given system Zeleny[32] states the new system can eliminate the resource con-strain through redesign then preferably obtain optimizationresults what can improves theTrade-off problems Zeleny [35]proposes the optimal portfolio resource allocation conceptwhich designs a resource integrated system that is individualresource levels cannot determine separately so new designsystems do not have trade-offs to consider Zeleny [33]develops a De Novo programming method for designing an

Low

High

High

For achieving the aspireddesired

level

Good

The best

The best

Efficiencies

Perfo

rman

ce

Figure 7Through De Novo programming achieving the aspirationgoal

optimal system by reshaping the feasible set [33] Firm canmake strategy alliances with partner of supply chain systemand achieves resource integration adjusting the businessgoal remodifying the business model and justifying targetcustomer services This study expects through De Novo pro-gramming approach to compute the data of resource integrat-ing and justify the resource allocationobjective alternativefrom a good level to best level thus achieving aspiration goal(see Figure 7)

8 Mathematical Problems in Engineering

46 Empirical Case Study

461 A Case Study of IC Production Firm A general case ofIC production problem involving two IC products GPS andcell phone of IC in quantities 1199091 and 1199092 each consuming fivedifferent resources (unitmarket prices of resources are given)The data is summarized as shown in Table 1

The costs of the given resources portfolio

(28 times 32) + (35 times 30) + (10 times 55)

+ (18 times 10) + (10 times 28) = $2956(10)

Unit costs of producing one unit goods of the two products

1199091 = (28 lowast 6) + (35 lowast 3) + (10 lowast 14) + (18 lowast 2)

+ (10 lowast 6) = $509

1199092 = (28 lowast 2) + (35 lowast 5) + (10 lowast 6) + (18 lowast 5)

+ (10 lowast 8) = $461

(11)

Expected profit margins (price-cost) arethe profit of 1199091 product = $549 minus $509 = $40unitthe profit of 1199092 product = $491 minus $461 = $30unit

Maximizing total value of function 11989111198911 = 401199091 + 301199092 (12)

Maximizing total quality index 11989121198912 = 81199091 + 101199092 (13)

Maximizing levels of two products calculated by De Novoprogramming

max 1198911 = 401199091 + 301199092

max 1198912 = 81199091 + 101199092

st 61199091 + 21199092 le 32

31199091 + 51199092 le 30

141199091 + 61199092 le 55

21199091 + 51199092 le 10

61199091 + 81199092 le 28

1199091 1199092 ge 0

(14)

Maximum 1198911 in profit

max 1198911 997888rarr 1199091 = 371

1199092 = 052

119891lowast

1= 40 times 371 + 30 times 052 = $164

(15)

Maximum 1198912 in total quality index

max 1198912 997888rarr 1199091 = 371

1199092 = 052

119891lowast

2= 8 times 371 + 10 times 052 = $3488

(16)

Minimizing the total cost by considering the followingconstraints

min 5091199091 + 4611199092

st 401199091 + 301199092 le 164

81199091 + 101199092 le 3488

(17)

Maximum 1198911 in profit

max 1198911 997888rarr 1199091 = 371

1199092 = 052

119891lowast

1= 40 times 371 + 30 times 052 = $164

(18)

Maximum 1198912 in total quality index

max 1198912 997888rarr 1199091 = 371

1199092 = 052

119891lowast

2= 8 times 371 + 10 times 052 = $3488

(19)

Cost of the newly designed system

(28 times 2328) + (35 times 1368) + (10 times 5506)

+ (18 times 1002) + (10 times 2642) = $21258(20)

The new portfolio of resources proposed by the consultant isshown in Table 2

The result of data analysis shows that the new planningsystem cost is $21258

The given resource portfolio cost is $2956 Comparisonbetween original and new planning model we found outthat the new planning model can reduce cost to $8305 andthat new resource planning model is one reliable modelConsequently the IC design service firm can follow the newplanningmodel ofDe novo programming to process resourceintegration

462 The Case of IC Design Service Firm The IC designservice firm is a professional workshop that focuses on theIC design task dependent on what design they can provideservice The IC design service task is also a project-basedplanning under a large RampDproject with business developingstrategy A project-based firm uses external delivery projectsfor business purposes [47 48] This study utilizes a real caseof IC design project management to practice on IC designservice firm Project planning may include many subprojectsthat depend on different cases of customerrsquos demand Centralfeatures of project management were identified according toindividual project uniqueness human resources of projectand business network complexity discontinuity of demandand relationships between other projects and considerablefinancial commitment of the parties [49 50] Conductingaspiration project planning under resource constraint is atarget issue for the IC design service firm This real caseincludes five different resources which included humanpower resource design equipments design materials special

Mathematical Problems in Engineering 9

Table 1 The IC of GPS and cell phone production requiring material

Unit price ($) Material of production processTechnological coefficients

Resource portfolio(units)(Resource requirement)

1199091

1199092

28 Silver wire 6 2 3235 Golden wire 3 5 3010 Silicon Crystal 14 6 5518 Chemistry material 2 5 1010 Conductor material 6 8 28

Table 2 The new resource portfolio planning of IC of GPS and cell phone

Unit price ($) Material of production processTechnological coefficients

Resource portfolio(units)(Resource requirement)

1199091

1199092

28 Silver wire 6 2 232835 Golden wire 3 5 136810 Silicon Crystal 14 6 550618 Chemistry material 2 5 100210 Conductor material 6 8 2642

technical human and design outsourcing with two differentproject teams for the A company customer to performthe IC design task The current study names the twoprojects as the 1198751 and 1198752 project team programs This realplanning program case utilizes the De Novo programmingfor adjustable resource allocation and seeks out minimumcost with maximum performance This work transitions thefive kinds of resources (design equipments design humanpower design material special technical human and design-outsourcing) in the case study to dollar amounts for easy costcalculation Design equipments cost is $380 per unit designhuman power labor cost is $240 per unit design materialresource cost is $200 special technical human cost is $260per unit and design outsourcing cost is $120 per unit

Costs of given resources portfolio

(380 times 26) + (240 times 50) + (200 times 30) + (120 times 10)

+ (260 times 16) = $33240(21)

Unit costs for running each project of the two projects

1198751 = (380 lowast 8) + (240 lowast 10) + (200 lowast 4)

+ (260 lowast 6) = $7800

1198752 = (380 lowast 6) + (240 lowast 4) + (200 lowast 4)

+ (120 lowast 4) = $4520

(22)

Expected profit margins (price cost) are

1198751 = $8200 minus $7800 = $400unit

1198752 = $4820 minus $4520 = $300unit(23)

Maximizing total value of function 1198911

1198911 = 4001199011 + 3001199012 (24)

Maximizing total quality index 1198912

1198912 = 81199011 + 101199012 (25)

Maximizing levels of the two projects calculated by De Novoprogramming

max 1198911 = 4001199011 + 3001199012

max 1198912 = 81199011 + 101199012

st 81199011 + 61199012 le 26

101199011 + 41199012 le 50

41199011 + 41199012 le 30

41199012 le 10

61199011 le 16

1199011 1199012 ge 0

(26)

Maximum 1198911 in profit

max 1198911 997888rarr 1199011 = 267

1199012 = 078

119891lowast

1= 400 times 267 + 300 times 078 = $1300

(27)

10 Mathematical Problems in Engineering

Table 3 1198751and 119875

2project team program require resource of IC design service

Unit price ($) Resources of IC designProject coefficients

Resource portfolio(units)

(Resource requirement)1198751

1198752

380 Design equipments 8 6 26240 Human power resource 10 4 50200 Design material 4 4 30120 Design outsource 0 4 10260 Special technical human 6 0 16

Table 4 The new resource portfolio planning model of 1198751and 119875

2project team program

Unit price ($) Resources of IC designProject coefficients

Resource portfolio(units)(Resource requirement)

1198751

1198752

380 Design equipments 8 6 26240 Human power resource 10 4 325200 Design material 4 4 13120 Design outsource 0 4 0260 Special technical human 6 0 195

Maximum 1198912 in total quality index

max 1198912 997888rarr 1199011 = 267

1199012 = 078

119891lowast

2= 8 times 267 + 10 times 078 = $2916

(28)

Minimizing the total cost by considering the followingconstraints

min 78001199011 + 45201199012

st 1198911 = 4001199011 + 3001199012 le 1300

1198912 = 81199011 + 101199012 le 2916

(29)

Maximum 1198911 in profit

max 1198911 997888rarr 1199011 = 325

1199012 = 0

119891lowast

1= 400 times 325 = $1300

(30)

Maximum 1198912 in total quality index

max 1198912 997888rarr 1199011 = 325

1199012 = 0

119891lowast

2= 8 times 325 = $26

(31)

Cost of new design planning

(380 times 26) + (240 times 325) + (200 times 13) + (260 times 195)

= $25350(32)

New portfolio of resources proposed by the consultant isshown in Table 4

47 Discussions

471 The General Case of IC Production Firm The datashow the empirical case study using the De Novo program-ming which remodifies the planning model and adjusts theresources allocation portfolio The new plan model offersthe best optimized resource integration and reduces losingand enhances firmrsquos competitiveness Technical problemsprogramming are discussed as follows

(i) The original plan needs a total cost of $2956 withgiven resources portfolios

[(28 times 32) + (35 times 30) + (10 times 55) + (18 times 10)

+ (10 times 28) = $2956] (33)

(ii) The new planning model of resource allocation onlyrequires cost of $21258

[(28 times 2328) + (35 times 1368) + (10 times 5506)

+ (18 times 1002) + (10 times 2642) = $21258] (34)

(iii) The newly planning model can save $8302 costConsequently firm seeks new planning model tointegrate resource

$2956 minus $21258 = $8302 (35)

(iv) The newly planning model use the De Novo pro-gramming to reduce resource loss and get betterperformance than the original planning model Thispaper also utilizes the empirical case examination ofDe Novo programming for IC design service firmperformance The new planning model obtains muchbetter resource integration

Mathematical Problems in Engineering 11

472 The Case of IC Design Service Firm Firms have massproduction capacity limitation per one time period due tolimited resources especially for the preceding competitivefacility location problem on the decentralized supply chain[51] The IC design service firm is a standard project-basedprogrammodel Many project teams program through coop-eration under a large design program for different customerare needed This study utilizes a case study of IC design firmfor planning an IC design to customer serviceThis case studyincludes two project teams The 1198751 and 1198752 project with twodifferent plan programming on IC design see (Table 3) Oneproject team leader prefers hiring a special technical engineerfor the IC design task that can maintain the IC design keytechnician and train other staffThe other project team leaderprefers outsourcing design that reduces cost The IC designservice firm chooses the alternative optimal project teambased on the result of De Novo programming

The resource portfolio of firmrsquos original cost is as follows

(380 times 26) + (240 times 50) + (200 times 30) + (120 times 10)

+ (260 times 16) = $33240(36)

The cost of newly planning model through the De Novoprogramming is as follows

(380 times 26) + (240 times 325) + (200 times 13) + (260 times 195)

= $25350(37)

The new planning model saves cost as follows

$33240 minus $25350 = $7890 (38)

The IC design firm adopting1198751 project team cost is as follows

1199011 (8 times 26) + (10 times 325) + (4 times 13) + (6 times 195) = $702(39)

The IC design firm adopting1198752 project team cost is as follows

1199012 (6 times 26) + (4 times 325) + (4 times 13) = $338 (40)

The result of data analysis shows through the De Novoprogramming method the IC design service firm that canfind out which project team is the optimal solution In thisempirical case adopting 1198752 project team can save design costmuchmore than1198751 projectThe new planningmodel reduces$7890 in cost compared with the original planning modelSo the newly planning model is the best choice for achievingaspiration goal

5 Conclusion

The IC semiconductor industry faces uncertainty anddynamic external environment especially at IC design servicefirm that needs one resource integrating programmingmodelfor new product development [52] Adopting an efficientstrategic alliancesresource integrating planning model is akey issue for achieving firmrsquos business goals especially astheir resource was constrainedThe present study considers a

planningmodel based on resource constraintsThis study uti-lizes the De Novo programming for achieving firmrsquos strategicgoal The De Novo programming can help firms find out anoptimal resource integration base on firm financial considerThis planning model provided reliable systematize approachthat combines alliances from external resources and changethem to internal resources application The project team1198752 adopting open innovation concept through outsourcingreduces design cost and enhances design performance DeNovo programming not only obtains optimal resource inte-gration but also promotes the conceptual of strategic alliancesfor firmrsquos cooperation between each other

51 Management Implication This research aims to create aresource integrating plan model through IC design servicefirm as an empirical case for examining the result of theplanning model The planning model is based on openinnovation theory exploring the IC design service firmwhichintegrates IC design resources and makes optimal resourcesintegrate in IC design service firm The planning model ofDe Novo programming is not only for IC design service firmbut also can apply to the other industrial implementationstrategic allianceresource integration This planning modelis a universal model for the other industry fields

52 Suggestion The IC design service industry is a veryspecial industry We suggest the extend to the other industrythat can find out different results Suggestion utilizes the otherapproach of MCDMmethod exploring different case studiesof industry

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

The authors appreciate two blind reviewers who have pro-vided very excellent comments These comments are verygood for the authors to improve the quality of the paperAlso thanks to the editor Professor Chang for offering lots ofexcellent suggestions that helped enhance this paperrsquos quality

References

[1] R Varadarajan ldquoOutsourcing think more expansivelyrdquo Journalof Business Research vol 62 no 11 pp 1165ndash1172 2009

[2] G Forgionne and Z Guo ldquoInternal supply chain coordinationin the electric utility industryrdquo European Journal of OperationalResearch vol 196 no 2 pp 619ndash627 2009

[3] R George and R Kabir ldquoBusiness groups and profit redistribu-tion a boon or bane for firmsrdquo Journal of Business Research vol61 no 9 pp 1004ndash1014 2008

[4] L Duckstein and S Opricovic ldquoMultiobjective optimization inriver basin developmentrdquoWater Resources Research vol 16 no1 pp 14ndash20 1980

12 Mathematical Problems in Engineering

[5] M Zeleny ldquoA case study in multiple objective design DeNovo programmingrdquo inMultiple Criteria Analysis OperationalMethods vol 1 pp 37ndash52 1981

[6] M Zeleny ldquoOptimal system design with multiple criteria DeNovo programming approachrdquo Engineering Costs and Produc-tion Economics vol 10 no 1 pp 89ndash94 1986

[7] S Christophersona M Kitsonb and J Michiec ldquoInnovationnetworks and knowledge exchangerdquo Cambridge Journal ofRegions Economy and Society vol 1 no 2 pp 165ndash173 2008

[8] H W Chesbrough Open Innovation The New Imperativefor Creating and Profiting from Technology Harvard BusinessSchool Press Boston Mass USA 2003

[9] J B Barney ldquoFirm resources and sustained competitive advan-tagerdquo Advances in Strategic Management vol 17 pp 203ndash2272000

[10] B Wernerfelt ldquoA resource-based view of the firmrdquo StrategicManagement Journal vol 5 no 2 pp 171ndash180 1984

[11] T R Holcomb and M A Hitt ldquoToward a model of strategicoutsourcingrdquo Journal of Operations Management vol 25 no 2pp 464ndash481 2007

[12] D J Collis ldquoA resource-based analysis of global competitionthe case of the bearings industryrdquo Strategic Management Jour-nal vol 12 no 1 pp 49ndash68 1991

[13] E Penrose The Theory of the Growth of the Firm John Wileyand Sons New York NY USA 1959

[14] E Anderson andH Gatignon ldquoModes of foreign entry a trans-action cost analysis and propositionsrdquo Journal of InternationalBusiness Studies vol 17 no 3 pp 1ndash26 1986

[15] J Chimhanzi and R E Morgan ldquoExplanations from themarketinghuman resources dyad formarketing strategy imple-mentation effectiveness in service firmsrdquo Journal of BusinessResearch vol 58 no 6 pp 787ndash796 2005

[16] J Fahy ldquoA resource-based analysis of sustainable competitiveadvantage in a global environmentrdquo International BusinessReview vol 11 no 1 pp 57ndash78 2002

[17] M A Harrison R E Hoskisson and D Ireland ldquoSynergies andpost acquisition performance differences versus similarities inresource allocationsrdquo Journal of Management vol 17 no 1 pp173ndash190 1991

[18] D J Teece G Pisano and A Shuen ldquoDynamic capabilities andstrategic managementrdquo Strategic Management Journal vol 18no 7 pp 509ndash533 1997

[19] V A Varma J F Pekny G E Blau and G V Reklaitis ldquoAframework for addressing stochastic and combinatorial aspectsof scheduling and resource allocation in pharmaceutical RampDpipelinesrdquoComputers andChemical Engineering vol 32 no 4-5pp 1000ndash1015 2008

[20] R Kolisch ldquoEfficient priority rules for the resource-constrainedproject scheduling problemrdquo Journal of Operations Manage-ment vol 14 no 3 pp 179ndash192 1996

[21] R Kolisch and S Hartmann ldquoExperimental investigationof heuristics for resource-constrained project scheduling anupdaterdquo European Journal of Operational Research vol 174 no1 pp 23ndash37 2006

[22] M J Leiblein ldquoThe choice of organizational governance formand performance predictions from transaction cost resource-based and real options theoriesrdquo Journal of Management vol29 no 6 pp 937ndash961 2003

[23] G CMoore and I Benbasat ldquoDevelopment of an instrument tomeasure the perceptions of adopting an information technologyinnovationrdquo Information Systems Research vol 2 no 3 pp 192ndash222 1991

[24] R E Hoskisson M A Hitt W P Wan and D Yiu ldquoTheoryand research in strategic management swings of a pendulumrdquoJournal of Management vol 25 no 3 pp 417ndash456 1999

[25] C C Miller and L B Cardinal ldquoStrategic planning and firmperformance a synthesis of more than two decades of researchrdquoAcademy of Management Journal vol 37 no 6 pp 1649ndash16651994

[26] S D Hunt A General Theory of Competition Sage ThousandOaks Calif USA 2000

[27] S Hart and C Banbury ldquoHow strategy-making processes canmake a differencerdquo Strategic Management Journal vol 15 no 4pp 251ndash269 1994

[28] E Drakopoulos ldquoEnterprise network planning and designmethodology and applicationrdquo Computer Communications vol22 no 4 pp 340ndash352 1999

[29] V Kumar B Maheshwari and U Kumar ldquoERP systems imple-mentation best practices in Canadian government organiza-tionsrdquoGovernment InformationQuarterly vol 19 no 2 pp 147ndash172 2002

[30] W R Lewis ldquoStrategic planningrdquo Hospital Materiel Manage-ment Quarterly vol 10 no 4 pp 57ndash63 1989

[31] D Wainwright and T Waring ldquoThree domains for imple-menting integrated information systems redressing the bal-ance between technology strategic and organisational analysisrdquoInternational Journal of InformationManagement vol 24 no 4pp 329ndash346 2004

[32] M Zeleny ldquoOptimal system design with multiple criteria DeNovo programming approachrdquo Engineering Costs and Produc-tion Economics vol 10 no 1 pp 89ndash95 1986

[33] M Zeleny ldquoOptimal given system versus designing optimalsystem the De Novo programming approachrdquo InternationalJournal of General System vol 17 no 3 pp 295ndash307 1990

[34] Y M Zhang G H Huang and X D Zhang ldquoInexact de Novoprogramming for water resources systems planningrdquo EuropeanJournal ofOperational Research vol 199 no 2 pp 531ndash541 2009

[35] M Zeleny ldquoTrade-offs free management via De Novo pro-grammingrdquo International Journal Operations and QuantitativeManagement vol 1 no 1 pp 3ndash13 1995

[36] H Chesbrough W Anhaverbeke and J West Eds Openinnovation Researching a New Paradigm Oxford UniversityPress 2006

[37] J West and S Gallagher ldquoChallenges of open innovation theparadox of firm investment in open-source softwarerdquo R amp DManagement vol 36 no 3 pp 319ndash331 2006

[38] E H Kessler P E Bierly and S Gopalakrishnan ldquoInternalversus external learning in new product development effects onspeed costs and competitive advantagerdquo R amp D Managementvol 30 no 3 pp 213ndash223 2000

[39] O Gassmann and E Enkel ldquoTowards a theory of open innova-tion three core process archetypesrdquo in Proceedings of the RampDManagement Conference (RADMA rsquo04) Lisbon Portugal 2004

[40] L V Kantorovich and T C Koopmans Problems of Applicationof Optimization Methods in Industry Federation of SwedishIndustry 1976

[41] T K Das and B-S Teng ldquoA resource-based theory of strategicalliancesrdquo Journal of Management vol 26 no 1 pp 31ndash61 2000

[42] Z Babic and I Pavic ldquoMulticriterial production planning byDe Novo programming approachrdquo International Journal ofProduction Economics vol 43 no 1 pp 59ndash66 1996

[43] J K C Chen and G H Tzeng ldquoPerspective strategic allianceand resource allocation in supply chain systems through the De

Mathematical Problems in Engineering 13

Novo programming approachrdquo International Journal Sustain-able Strategic Management vol 1 no 3 pp 320ndash339 2009

[44] J-J Huang G-H Tzeng and C-S Ong ldquoMotivation andresource-allocation for strategic alliances through the De NovoperspectiverdquoMathematical and ComputerModelling vol 41 no6-7 pp 711ndash721 2005

[45] J-J Huang G-H Tzeng and C-S Ong ldquoChoosing bestalliance partners and allocating optimal alliance resources usingthe fuzzymulti-objective dummyprogrammingmodelrdquo Journalof the Operational Research Society vol 57 no 10 pp 1216ndash12232006

[46] R M Grant ldquoThe resource-based theory competitive advan-tage implications for strategy formulationrdquo California Manage-ment Review vol 33 no 3 pp 114ndash135 1991

[47] K A Artto and K Wikstrom ldquoWhat is project businessrdquoInternational Journal of Project Management vol 23 no 5 pp343ndash353 2005

[48] J Soderlund ldquoOn the broadening scope of the research onprojects a review and a model for analysisrdquo InternationalJournal of ProjectManagement vol 22 no 8 pp 655ndash667 2004

[49] B Cova P Ghauri and R Salle Project Marketing BeyondCompetitive Bidding JohnWiley amp Sons Chichester UK 2002

[50] T Mandjak and Z Veres ldquoThe D-U-C model and the stages ofthe project marketing processrdquo in Proceedings of the 14th IMPAnnual Conference Proceedings H K Nummela Ed pp 471ndash490 1998

[51] Q Meng Y Huang and R L Cheu ldquoCompetitive facilitylocation on decentralized supply chainsrdquo European Journal ofOperational Research vol 196 no 2 pp 487ndash499 2009

[52] H-M Hsu and W-P Wang ldquoDynamic programming fordelayed product differentiationrdquo European Journal of Opera-tional Research vol 156 no 1 pp 183ndash193 2004

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Mathematical Problems in Engineering

Hindawi Publishing Corporationhttpwwwhindawicom

Differential EquationsInternational Journal of

Volume 2014

Applied MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Mathematical PhysicsAdvances in

Complex AnalysisJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

OptimizationJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Operations ResearchAdvances in

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Function Spaces

Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of Mathematics and Mathematical Sciences

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

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Algebra

Discrete Dynamics in Nature and Society

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

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Decision SciencesAdvances in

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Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Stochastic AnalysisInternational Journal of

Mathematical Problems in Engineering 5

The firm should seek allianceintegration with the otherpartner

From the RBV view firms seek resource alliances capa-bilities by allying with a partner to create synergies based onRBV theory The rule of the RBV can also be modified asif allianceintegration benefit is larger than individual firmrsquosprofit summary (ge sum119873

119894=1)

The firm should seek allianceintegration with the otherfirms

Now we involve the cost and RBV theory into the DeNovo programming if the firm alternatives are based on twodifferent resources 119878 and 119879 the rule of resource integratingcan be expressed as follows

if 119864 (119878 cup 119879) minus 119880 (119862119878119879) gt 119864 (119878) + 119864 (119879) minus 119880 (119862119878) minus 119880 (119862119879) (2)

then the firm should seek resource integration with partnerof industry cluster

Also it can express a general formula as follows

if 119864 (1198781 cup 1198782 sdot sdot sdot cup 119878119873) minus 119880 (119862 alliance cost )

ge

119873

sum119894=1

[119864 (119878119894) minus 119880 (119878119894)] 119894 = 1 2 119873(3)

where119864(sdot) is the benefit function119880(sdot) is the cost function119862119878and 119862119879 denote the total design cost in 119878 and 119879 respectivelyand 119862119878119879 denotes the allianceintegration cost between 119878

and 119879 The probability of 119878 and 119879 events seeking strategicallianceresource integration can be demoted respectively as

119901 (119904) = 1 120582119864 (119878 cup 119879) minus 120579119880 (119862119878119879) gt 119864 (119878) minus 119880 (119862119878)

0 120582119864 (119878 cup 119879) minus 120579119880 (119862119878119879) lt 119864 (119878) minus 119880 (119862119878)

(4a)

119901 (119879) =

1 (1 minus 120582) 119864 (119878 cup 119879) minus (1 minus 120579)119880 (119862119878119879)

gt 119864 (119879) minus 119880 (119862119879)

0 (1 minus 120582) 119864 (119878 cup 119879) minus (1 minus 120579)119880 (119862119878119879)

lt 119864 (119879) minus 119880 (119862119879)

(4b)

where 120582 denotes the percentage of increasing alliance benefitin 119878 and 120579 denotes the percentage of reducing alliance costin 119878 Now we can involve De Novo programming into theplanning model of resource integrating that can be expressedas

max 119864 (119878 cup 119879) minus 119880 (119862119878119879)

st 119908119909 le 119861 119909 ge 0(5)

where 119908 = 119901119860 = (1199081 119908119899) isin 119877119899 and 119901 = (1199011 119901119898) isin

119877119898 and 119861 isin 119877 present the unit price of resources and the totalavailable budget Then the knapsack solution is

119909lowast= [0

119861

119862119896 0]

119879

(6)

where

119888119896

119888lowast119896

= max119895

(119888119895

119888lowast119895

) (7)

Max 0

Max

Feasiblespace

120587R

120587AA

C

B

Figure 3 Feasible options obtained using De Novo programming

The optimal solution to (5) is given by (6) and

119887lowast= 119860119909lowast (8)

The final allianceintegration benefit (Ψ(119878lowast)) in 119878 is

Ψ (119878lowast) = 1198941015840119887lowast119880(minus119862119878119879) (9)

where 119894 is the identity column vector According to (9) wecan judge whether or not the firm should seek strategicallianceresource integration by (3) (4a) and (4b) Further-more using De Novo programming we can easily achieveoptimal resource allocation and create synergies betweenalliances The difference between traditional mathematicprogramming andDeNovo programming lies in the ability oftheDeNovoprogramming to redefine its boundaries throughsystem redesign reconfiguration or reshaping [33] Figure 3shows the difference in feasible options space obtainedthrough De Novo programming method

The greatest difference between Figures 2 and 3 is thatthe unavailable solutions before were made available throughDe Novo programming now In other words the idealpoint 119862 is the goal of optimal solution through strategicallianceresource integrating approach

4 Empirical Study Practice

It is commonly believed that integrating IC design humanresources into IC semiconductor supply chain system canenhance operation performance and such integration wouldrequire a complete planning model to ensure effectivenessThe proposed planning model is applied to IC design servicefirms in Taiwan as a empirical case study to examine theplanning model feasibility

41 The Case Study of Taiwanrsquos IC Service Design FirmTaiwanrsquos semiconductors industry is a globalization businessspecializing as the worldrsquos number one of DRAM IC foundrymanufacturer The IC semiconductors industry is a completesupply chain that includes IC design service supplier andcustomer The IC supply chain system developed becamefirms cluster with difference supply chain speciality needs

6 Mathematical Problems in Engineering

IC design service

Supplier Customer

Servicecluster

Suppliercluster

Customercluster

Figure 4 The supply chain of IC semiconductor industry

(see Figure 4) The IC design service is one of semiconductormanufacturing processing before launching into IC foundryThe IC design service firms obtain the order from customerand through project management controls the design sched-ule for customer needs More than 365 IC design servicefirms have been established around Hsinchu Science Parkin Taiwan This IC service design firm cluster situation isvery special in the world Consequently studying Taiwanrsquos ICdesign service firms is a very interesting empirical case

42 The Problems of IC Semiconductor Industry CurrentlyTaiwanrsquos semiconductors face a very slow economics situationof business operation Government release encourage policytry to save the semiconductor industry but these policies wasfail The economics of semiconductor continues going downof economic curve The IC industry external environmentalfactors are multiples and dynamics specializing on theIC design service sector Also the IC design service firmrequires one high performance project management to helpmanage related task Integrating internal human resource andleveraging external resource and adjusting business strategyformatch customer needs of IC designmarket As such firmsshould offer just-in-time mechanism of services for productsdelivery and quick response system for customers A little losson service process could bring the result of lose customerTherefore IC design firm should determine to operate atlowest cost and high quality level for IC design service Highperformance IC design service systems should be consideredin the semiconductor supply chain system

43 IC Design Service Firms The IC design skill is one of keytechnical of IC semiconductor manufacturing system It is

a new business model emerging as a vertical disintegrationproduct in the IC semiconductor industry The IC semicon-ductor combines multiprocesses into one complete systemincluding IC design and an IDM (integrated device manu-facturer) fabrication assembly test in the 1970s IC-designsand IDM were separated in the 1980s from one complete setsystem to develop two subsystems of IC design and IDMfabrication and continues developing including IC designand IDM fabrication and assembly The IC semiconductorindustry after the 1980s modified processing to include thesystem IC design IDM fabrication IC foundry IC assemblyand IC test on the subsystem The IC design subsystem after2001 extends from one to three parts including SiP (systemin package) design system and IC design The IC designservice firm has become an important process of the ICsemiconductors industry Figure 5 shows IC design servicedevelopment trajectory from 1960 to 2013

The IC design service firm is a high technology knowl-edge industry The IC design service firm requires pro-fessional human design who must have engineering back-ground specialized design technology knowledge and alsoneed high speed internet networks a convenient communi-cation digital platform and a high operation performanceThis study expects a building of an MCDM model with DeNovo programming for integrating the resource of IC designservice firmsThe IC design service firm is multidimensionaland highly competitive focusing on special professionaltechniques and quick response of design services De Novoprogramming helps to plan service model for achievingaspiration levels of the IC design service firms

44 The Relationship of IC Design Service Supplier andCustomer The IC semiconductors industry is a multipleproduction process that includes design fabrication foundrytesting assembly and delivery of products to customer TheIC design service is a substructure under the IC supplierproviding IC design service for supplier or customer The ICdesign service firms offer system in package (SiP) design todirect customer or IC design and IC layout design for supplier(see Figure 6) The IC design service firms offers value addedat each design service process for example highlightingperformance powerful function low production cost systemon chip (SoC) and redownsizing IC design space Figure 6shows relationship between of IC design service supplier andcustomer

The IC design service firms not only provides designservice for IC semiconductors manufacturing systems butalso offers new innovative ideasfunction on IC chip forcustomer The IC design service firms generally providesprofessional IC design knowledge and IC foundry techniquesthat can obtain greater economics efficiency of semiconduc-tors production the result should be increased IC design ser-vice business opportunity Consequently adopting innovativeideas becomes a very important task in IC design serviceprocessing

45 Through De Novo Programming Achieving AspirationGoal This study expects through De Novo programming to

Mathematical Problems in Engineering 7

After 20001980s to 1990sBefore 1970s

IC design

System IC-ASSP IC-ASIC SOC-IP

SystemSystemSystemSystemSystemIC designIDM fabassembly

test

DS

IC design

IDM fab

Foundry

Assembly

Test

IC design

IDM fab

Foundry

Assembly

Test

IC design

IDM fab

Assembly

SystemIC designIDM fabassembly

test

20001990198019701960 2013

SIP

Figure 5 The IC design service development trajectory from 1960 to 2013 year

IC designservice

Supplier CustomerFinished products

SiP design service

Design requirement IC design service

Order requirement

IC layout order

Figure 6 The relationship of IC design service supplier andcustomer

achieve optimal resource integrating purpose The IC designservice firm must consider resource constrain under mul-tiple objectives alternatives before making decisions Firmface multiple objectives and evaluation criteria of problemnamely dealing with resources integrating and adjustingbusiness strategic to achieve the aspiration goal of businessIn general through trade-off skill it is almost impossible toobtain optimization of all criteria of a given system Zeleny[32] states the new system can eliminate the resource con-strain through redesign then preferably obtain optimizationresults what can improves theTrade-off problems Zeleny [35]proposes the optimal portfolio resource allocation conceptwhich designs a resource integrated system that is individualresource levels cannot determine separately so new designsystems do not have trade-offs to consider Zeleny [33]develops a De Novo programming method for designing an

Low

High

High

For achieving the aspireddesired

level

Good

The best

The best

Efficiencies

Perfo

rman

ce

Figure 7Through De Novo programming achieving the aspirationgoal

optimal system by reshaping the feasible set [33] Firm canmake strategy alliances with partner of supply chain systemand achieves resource integration adjusting the businessgoal remodifying the business model and justifying targetcustomer services This study expects through De Novo pro-gramming approach to compute the data of resource integrat-ing and justify the resource allocationobjective alternativefrom a good level to best level thus achieving aspiration goal(see Figure 7)

8 Mathematical Problems in Engineering

46 Empirical Case Study

461 A Case Study of IC Production Firm A general case ofIC production problem involving two IC products GPS andcell phone of IC in quantities 1199091 and 1199092 each consuming fivedifferent resources (unitmarket prices of resources are given)The data is summarized as shown in Table 1

The costs of the given resources portfolio

(28 times 32) + (35 times 30) + (10 times 55)

+ (18 times 10) + (10 times 28) = $2956(10)

Unit costs of producing one unit goods of the two products

1199091 = (28 lowast 6) + (35 lowast 3) + (10 lowast 14) + (18 lowast 2)

+ (10 lowast 6) = $509

1199092 = (28 lowast 2) + (35 lowast 5) + (10 lowast 6) + (18 lowast 5)

+ (10 lowast 8) = $461

(11)

Expected profit margins (price-cost) arethe profit of 1199091 product = $549 minus $509 = $40unitthe profit of 1199092 product = $491 minus $461 = $30unit

Maximizing total value of function 11989111198911 = 401199091 + 301199092 (12)

Maximizing total quality index 11989121198912 = 81199091 + 101199092 (13)

Maximizing levels of two products calculated by De Novoprogramming

max 1198911 = 401199091 + 301199092

max 1198912 = 81199091 + 101199092

st 61199091 + 21199092 le 32

31199091 + 51199092 le 30

141199091 + 61199092 le 55

21199091 + 51199092 le 10

61199091 + 81199092 le 28

1199091 1199092 ge 0

(14)

Maximum 1198911 in profit

max 1198911 997888rarr 1199091 = 371

1199092 = 052

119891lowast

1= 40 times 371 + 30 times 052 = $164

(15)

Maximum 1198912 in total quality index

max 1198912 997888rarr 1199091 = 371

1199092 = 052

119891lowast

2= 8 times 371 + 10 times 052 = $3488

(16)

Minimizing the total cost by considering the followingconstraints

min 5091199091 + 4611199092

st 401199091 + 301199092 le 164

81199091 + 101199092 le 3488

(17)

Maximum 1198911 in profit

max 1198911 997888rarr 1199091 = 371

1199092 = 052

119891lowast

1= 40 times 371 + 30 times 052 = $164

(18)

Maximum 1198912 in total quality index

max 1198912 997888rarr 1199091 = 371

1199092 = 052

119891lowast

2= 8 times 371 + 10 times 052 = $3488

(19)

Cost of the newly designed system

(28 times 2328) + (35 times 1368) + (10 times 5506)

+ (18 times 1002) + (10 times 2642) = $21258(20)

The new portfolio of resources proposed by the consultant isshown in Table 2

The result of data analysis shows that the new planningsystem cost is $21258

The given resource portfolio cost is $2956 Comparisonbetween original and new planning model we found outthat the new planning model can reduce cost to $8305 andthat new resource planning model is one reliable modelConsequently the IC design service firm can follow the newplanningmodel ofDe novo programming to process resourceintegration

462 The Case of IC Design Service Firm The IC designservice firm is a professional workshop that focuses on theIC design task dependent on what design they can provideservice The IC design service task is also a project-basedplanning under a large RampDproject with business developingstrategy A project-based firm uses external delivery projectsfor business purposes [47 48] This study utilizes a real caseof IC design project management to practice on IC designservice firm Project planning may include many subprojectsthat depend on different cases of customerrsquos demand Centralfeatures of project management were identified according toindividual project uniqueness human resources of projectand business network complexity discontinuity of demandand relationships between other projects and considerablefinancial commitment of the parties [49 50] Conductingaspiration project planning under resource constraint is atarget issue for the IC design service firm This real caseincludes five different resources which included humanpower resource design equipments design materials special

Mathematical Problems in Engineering 9

Table 1 The IC of GPS and cell phone production requiring material

Unit price ($) Material of production processTechnological coefficients

Resource portfolio(units)(Resource requirement)

1199091

1199092

28 Silver wire 6 2 3235 Golden wire 3 5 3010 Silicon Crystal 14 6 5518 Chemistry material 2 5 1010 Conductor material 6 8 28

Table 2 The new resource portfolio planning of IC of GPS and cell phone

Unit price ($) Material of production processTechnological coefficients

Resource portfolio(units)(Resource requirement)

1199091

1199092

28 Silver wire 6 2 232835 Golden wire 3 5 136810 Silicon Crystal 14 6 550618 Chemistry material 2 5 100210 Conductor material 6 8 2642

technical human and design outsourcing with two differentproject teams for the A company customer to performthe IC design task The current study names the twoprojects as the 1198751 and 1198752 project team programs This realplanning program case utilizes the De Novo programmingfor adjustable resource allocation and seeks out minimumcost with maximum performance This work transitions thefive kinds of resources (design equipments design humanpower design material special technical human and design-outsourcing) in the case study to dollar amounts for easy costcalculation Design equipments cost is $380 per unit designhuman power labor cost is $240 per unit design materialresource cost is $200 special technical human cost is $260per unit and design outsourcing cost is $120 per unit

Costs of given resources portfolio

(380 times 26) + (240 times 50) + (200 times 30) + (120 times 10)

+ (260 times 16) = $33240(21)

Unit costs for running each project of the two projects

1198751 = (380 lowast 8) + (240 lowast 10) + (200 lowast 4)

+ (260 lowast 6) = $7800

1198752 = (380 lowast 6) + (240 lowast 4) + (200 lowast 4)

+ (120 lowast 4) = $4520

(22)

Expected profit margins (price cost) are

1198751 = $8200 minus $7800 = $400unit

1198752 = $4820 minus $4520 = $300unit(23)

Maximizing total value of function 1198911

1198911 = 4001199011 + 3001199012 (24)

Maximizing total quality index 1198912

1198912 = 81199011 + 101199012 (25)

Maximizing levels of the two projects calculated by De Novoprogramming

max 1198911 = 4001199011 + 3001199012

max 1198912 = 81199011 + 101199012

st 81199011 + 61199012 le 26

101199011 + 41199012 le 50

41199011 + 41199012 le 30

41199012 le 10

61199011 le 16

1199011 1199012 ge 0

(26)

Maximum 1198911 in profit

max 1198911 997888rarr 1199011 = 267

1199012 = 078

119891lowast

1= 400 times 267 + 300 times 078 = $1300

(27)

10 Mathematical Problems in Engineering

Table 3 1198751and 119875

2project team program require resource of IC design service

Unit price ($) Resources of IC designProject coefficients

Resource portfolio(units)

(Resource requirement)1198751

1198752

380 Design equipments 8 6 26240 Human power resource 10 4 50200 Design material 4 4 30120 Design outsource 0 4 10260 Special technical human 6 0 16

Table 4 The new resource portfolio planning model of 1198751and 119875

2project team program

Unit price ($) Resources of IC designProject coefficients

Resource portfolio(units)(Resource requirement)

1198751

1198752

380 Design equipments 8 6 26240 Human power resource 10 4 325200 Design material 4 4 13120 Design outsource 0 4 0260 Special technical human 6 0 195

Maximum 1198912 in total quality index

max 1198912 997888rarr 1199011 = 267

1199012 = 078

119891lowast

2= 8 times 267 + 10 times 078 = $2916

(28)

Minimizing the total cost by considering the followingconstraints

min 78001199011 + 45201199012

st 1198911 = 4001199011 + 3001199012 le 1300

1198912 = 81199011 + 101199012 le 2916

(29)

Maximum 1198911 in profit

max 1198911 997888rarr 1199011 = 325

1199012 = 0

119891lowast

1= 400 times 325 = $1300

(30)

Maximum 1198912 in total quality index

max 1198912 997888rarr 1199011 = 325

1199012 = 0

119891lowast

2= 8 times 325 = $26

(31)

Cost of new design planning

(380 times 26) + (240 times 325) + (200 times 13) + (260 times 195)

= $25350(32)

New portfolio of resources proposed by the consultant isshown in Table 4

47 Discussions

471 The General Case of IC Production Firm The datashow the empirical case study using the De Novo program-ming which remodifies the planning model and adjusts theresources allocation portfolio The new plan model offersthe best optimized resource integration and reduces losingand enhances firmrsquos competitiveness Technical problemsprogramming are discussed as follows

(i) The original plan needs a total cost of $2956 withgiven resources portfolios

[(28 times 32) + (35 times 30) + (10 times 55) + (18 times 10)

+ (10 times 28) = $2956] (33)

(ii) The new planning model of resource allocation onlyrequires cost of $21258

[(28 times 2328) + (35 times 1368) + (10 times 5506)

+ (18 times 1002) + (10 times 2642) = $21258] (34)

(iii) The newly planning model can save $8302 costConsequently firm seeks new planning model tointegrate resource

$2956 minus $21258 = $8302 (35)

(iv) The newly planning model use the De Novo pro-gramming to reduce resource loss and get betterperformance than the original planning model Thispaper also utilizes the empirical case examination ofDe Novo programming for IC design service firmperformance The new planning model obtains muchbetter resource integration

Mathematical Problems in Engineering 11

472 The Case of IC Design Service Firm Firms have massproduction capacity limitation per one time period due tolimited resources especially for the preceding competitivefacility location problem on the decentralized supply chain[51] The IC design service firm is a standard project-basedprogrammodel Many project teams program through coop-eration under a large design program for different customerare needed This study utilizes a case study of IC design firmfor planning an IC design to customer serviceThis case studyincludes two project teams The 1198751 and 1198752 project with twodifferent plan programming on IC design see (Table 3) Oneproject team leader prefers hiring a special technical engineerfor the IC design task that can maintain the IC design keytechnician and train other staffThe other project team leaderprefers outsourcing design that reduces cost The IC designservice firm chooses the alternative optimal project teambased on the result of De Novo programming

The resource portfolio of firmrsquos original cost is as follows

(380 times 26) + (240 times 50) + (200 times 30) + (120 times 10)

+ (260 times 16) = $33240(36)

The cost of newly planning model through the De Novoprogramming is as follows

(380 times 26) + (240 times 325) + (200 times 13) + (260 times 195)

= $25350(37)

The new planning model saves cost as follows

$33240 minus $25350 = $7890 (38)

The IC design firm adopting1198751 project team cost is as follows

1199011 (8 times 26) + (10 times 325) + (4 times 13) + (6 times 195) = $702(39)

The IC design firm adopting1198752 project team cost is as follows

1199012 (6 times 26) + (4 times 325) + (4 times 13) = $338 (40)

The result of data analysis shows through the De Novoprogramming method the IC design service firm that canfind out which project team is the optimal solution In thisempirical case adopting 1198752 project team can save design costmuchmore than1198751 projectThe new planningmodel reduces$7890 in cost compared with the original planning modelSo the newly planning model is the best choice for achievingaspiration goal

5 Conclusion

The IC semiconductor industry faces uncertainty anddynamic external environment especially at IC design servicefirm that needs one resource integrating programmingmodelfor new product development [52] Adopting an efficientstrategic alliancesresource integrating planning model is akey issue for achieving firmrsquos business goals especially astheir resource was constrainedThe present study considers a

planningmodel based on resource constraintsThis study uti-lizes the De Novo programming for achieving firmrsquos strategicgoal The De Novo programming can help firms find out anoptimal resource integration base on firm financial considerThis planning model provided reliable systematize approachthat combines alliances from external resources and changethem to internal resources application The project team1198752 adopting open innovation concept through outsourcingreduces design cost and enhances design performance DeNovo programming not only obtains optimal resource inte-gration but also promotes the conceptual of strategic alliancesfor firmrsquos cooperation between each other

51 Management Implication This research aims to create aresource integrating plan model through IC design servicefirm as an empirical case for examining the result of theplanning model The planning model is based on openinnovation theory exploring the IC design service firmwhichintegrates IC design resources and makes optimal resourcesintegrate in IC design service firm The planning model ofDe Novo programming is not only for IC design service firmbut also can apply to the other industrial implementationstrategic allianceresource integration This planning modelis a universal model for the other industry fields

52 Suggestion The IC design service industry is a veryspecial industry We suggest the extend to the other industrythat can find out different results Suggestion utilizes the otherapproach of MCDMmethod exploring different case studiesof industry

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

The authors appreciate two blind reviewers who have pro-vided very excellent comments These comments are verygood for the authors to improve the quality of the paperAlso thanks to the editor Professor Chang for offering lots ofexcellent suggestions that helped enhance this paperrsquos quality

References

[1] R Varadarajan ldquoOutsourcing think more expansivelyrdquo Journalof Business Research vol 62 no 11 pp 1165ndash1172 2009

[2] G Forgionne and Z Guo ldquoInternal supply chain coordinationin the electric utility industryrdquo European Journal of OperationalResearch vol 196 no 2 pp 619ndash627 2009

[3] R George and R Kabir ldquoBusiness groups and profit redistribu-tion a boon or bane for firmsrdquo Journal of Business Research vol61 no 9 pp 1004ndash1014 2008

[4] L Duckstein and S Opricovic ldquoMultiobjective optimization inriver basin developmentrdquoWater Resources Research vol 16 no1 pp 14ndash20 1980

12 Mathematical Problems in Engineering

[5] M Zeleny ldquoA case study in multiple objective design DeNovo programmingrdquo inMultiple Criteria Analysis OperationalMethods vol 1 pp 37ndash52 1981

[6] M Zeleny ldquoOptimal system design with multiple criteria DeNovo programming approachrdquo Engineering Costs and Produc-tion Economics vol 10 no 1 pp 89ndash94 1986

[7] S Christophersona M Kitsonb and J Michiec ldquoInnovationnetworks and knowledge exchangerdquo Cambridge Journal ofRegions Economy and Society vol 1 no 2 pp 165ndash173 2008

[8] H W Chesbrough Open Innovation The New Imperativefor Creating and Profiting from Technology Harvard BusinessSchool Press Boston Mass USA 2003

[9] J B Barney ldquoFirm resources and sustained competitive advan-tagerdquo Advances in Strategic Management vol 17 pp 203ndash2272000

[10] B Wernerfelt ldquoA resource-based view of the firmrdquo StrategicManagement Journal vol 5 no 2 pp 171ndash180 1984

[11] T R Holcomb and M A Hitt ldquoToward a model of strategicoutsourcingrdquo Journal of Operations Management vol 25 no 2pp 464ndash481 2007

[12] D J Collis ldquoA resource-based analysis of global competitionthe case of the bearings industryrdquo Strategic Management Jour-nal vol 12 no 1 pp 49ndash68 1991

[13] E Penrose The Theory of the Growth of the Firm John Wileyand Sons New York NY USA 1959

[14] E Anderson andH Gatignon ldquoModes of foreign entry a trans-action cost analysis and propositionsrdquo Journal of InternationalBusiness Studies vol 17 no 3 pp 1ndash26 1986

[15] J Chimhanzi and R E Morgan ldquoExplanations from themarketinghuman resources dyad formarketing strategy imple-mentation effectiveness in service firmsrdquo Journal of BusinessResearch vol 58 no 6 pp 787ndash796 2005

[16] J Fahy ldquoA resource-based analysis of sustainable competitiveadvantage in a global environmentrdquo International BusinessReview vol 11 no 1 pp 57ndash78 2002

[17] M A Harrison R E Hoskisson and D Ireland ldquoSynergies andpost acquisition performance differences versus similarities inresource allocationsrdquo Journal of Management vol 17 no 1 pp173ndash190 1991

[18] D J Teece G Pisano and A Shuen ldquoDynamic capabilities andstrategic managementrdquo Strategic Management Journal vol 18no 7 pp 509ndash533 1997

[19] V A Varma J F Pekny G E Blau and G V Reklaitis ldquoAframework for addressing stochastic and combinatorial aspectsof scheduling and resource allocation in pharmaceutical RampDpipelinesrdquoComputers andChemical Engineering vol 32 no 4-5pp 1000ndash1015 2008

[20] R Kolisch ldquoEfficient priority rules for the resource-constrainedproject scheduling problemrdquo Journal of Operations Manage-ment vol 14 no 3 pp 179ndash192 1996

[21] R Kolisch and S Hartmann ldquoExperimental investigationof heuristics for resource-constrained project scheduling anupdaterdquo European Journal of Operational Research vol 174 no1 pp 23ndash37 2006

[22] M J Leiblein ldquoThe choice of organizational governance formand performance predictions from transaction cost resource-based and real options theoriesrdquo Journal of Management vol29 no 6 pp 937ndash961 2003

[23] G CMoore and I Benbasat ldquoDevelopment of an instrument tomeasure the perceptions of adopting an information technologyinnovationrdquo Information Systems Research vol 2 no 3 pp 192ndash222 1991

[24] R E Hoskisson M A Hitt W P Wan and D Yiu ldquoTheoryand research in strategic management swings of a pendulumrdquoJournal of Management vol 25 no 3 pp 417ndash456 1999

[25] C C Miller and L B Cardinal ldquoStrategic planning and firmperformance a synthesis of more than two decades of researchrdquoAcademy of Management Journal vol 37 no 6 pp 1649ndash16651994

[26] S D Hunt A General Theory of Competition Sage ThousandOaks Calif USA 2000

[27] S Hart and C Banbury ldquoHow strategy-making processes canmake a differencerdquo Strategic Management Journal vol 15 no 4pp 251ndash269 1994

[28] E Drakopoulos ldquoEnterprise network planning and designmethodology and applicationrdquo Computer Communications vol22 no 4 pp 340ndash352 1999

[29] V Kumar B Maheshwari and U Kumar ldquoERP systems imple-mentation best practices in Canadian government organiza-tionsrdquoGovernment InformationQuarterly vol 19 no 2 pp 147ndash172 2002

[30] W R Lewis ldquoStrategic planningrdquo Hospital Materiel Manage-ment Quarterly vol 10 no 4 pp 57ndash63 1989

[31] D Wainwright and T Waring ldquoThree domains for imple-menting integrated information systems redressing the bal-ance between technology strategic and organisational analysisrdquoInternational Journal of InformationManagement vol 24 no 4pp 329ndash346 2004

[32] M Zeleny ldquoOptimal system design with multiple criteria DeNovo programming approachrdquo Engineering Costs and Produc-tion Economics vol 10 no 1 pp 89ndash95 1986

[33] M Zeleny ldquoOptimal given system versus designing optimalsystem the De Novo programming approachrdquo InternationalJournal of General System vol 17 no 3 pp 295ndash307 1990

[34] Y M Zhang G H Huang and X D Zhang ldquoInexact de Novoprogramming for water resources systems planningrdquo EuropeanJournal ofOperational Research vol 199 no 2 pp 531ndash541 2009

[35] M Zeleny ldquoTrade-offs free management via De Novo pro-grammingrdquo International Journal Operations and QuantitativeManagement vol 1 no 1 pp 3ndash13 1995

[36] H Chesbrough W Anhaverbeke and J West Eds Openinnovation Researching a New Paradigm Oxford UniversityPress 2006

[37] J West and S Gallagher ldquoChallenges of open innovation theparadox of firm investment in open-source softwarerdquo R amp DManagement vol 36 no 3 pp 319ndash331 2006

[38] E H Kessler P E Bierly and S Gopalakrishnan ldquoInternalversus external learning in new product development effects onspeed costs and competitive advantagerdquo R amp D Managementvol 30 no 3 pp 213ndash223 2000

[39] O Gassmann and E Enkel ldquoTowards a theory of open innova-tion three core process archetypesrdquo in Proceedings of the RampDManagement Conference (RADMA rsquo04) Lisbon Portugal 2004

[40] L V Kantorovich and T C Koopmans Problems of Applicationof Optimization Methods in Industry Federation of SwedishIndustry 1976

[41] T K Das and B-S Teng ldquoA resource-based theory of strategicalliancesrdquo Journal of Management vol 26 no 1 pp 31ndash61 2000

[42] Z Babic and I Pavic ldquoMulticriterial production planning byDe Novo programming approachrdquo International Journal ofProduction Economics vol 43 no 1 pp 59ndash66 1996

[43] J K C Chen and G H Tzeng ldquoPerspective strategic allianceand resource allocation in supply chain systems through the De

Mathematical Problems in Engineering 13

Novo programming approachrdquo International Journal Sustain-able Strategic Management vol 1 no 3 pp 320ndash339 2009

[44] J-J Huang G-H Tzeng and C-S Ong ldquoMotivation andresource-allocation for strategic alliances through the De NovoperspectiverdquoMathematical and ComputerModelling vol 41 no6-7 pp 711ndash721 2005

[45] J-J Huang G-H Tzeng and C-S Ong ldquoChoosing bestalliance partners and allocating optimal alliance resources usingthe fuzzymulti-objective dummyprogrammingmodelrdquo Journalof the Operational Research Society vol 57 no 10 pp 1216ndash12232006

[46] R M Grant ldquoThe resource-based theory competitive advan-tage implications for strategy formulationrdquo California Manage-ment Review vol 33 no 3 pp 114ndash135 1991

[47] K A Artto and K Wikstrom ldquoWhat is project businessrdquoInternational Journal of Project Management vol 23 no 5 pp343ndash353 2005

[48] J Soderlund ldquoOn the broadening scope of the research onprojects a review and a model for analysisrdquo InternationalJournal of ProjectManagement vol 22 no 8 pp 655ndash667 2004

[49] B Cova P Ghauri and R Salle Project Marketing BeyondCompetitive Bidding JohnWiley amp Sons Chichester UK 2002

[50] T Mandjak and Z Veres ldquoThe D-U-C model and the stages ofthe project marketing processrdquo in Proceedings of the 14th IMPAnnual Conference Proceedings H K Nummela Ed pp 471ndash490 1998

[51] Q Meng Y Huang and R L Cheu ldquoCompetitive facilitylocation on decentralized supply chainsrdquo European Journal ofOperational Research vol 196 no 2 pp 487ndash499 2009

[52] H-M Hsu and W-P Wang ldquoDynamic programming fordelayed product differentiationrdquo European Journal of Opera-tional Research vol 156 no 1 pp 183ndash193 2004

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Mathematical Problems in Engineering

Hindawi Publishing Corporationhttpwwwhindawicom

Differential EquationsInternational Journal of

Volume 2014

Applied MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Mathematical PhysicsAdvances in

Complex AnalysisJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

OptimizationJournal of

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CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

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Operations ResearchAdvances in

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Function Spaces

Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of Mathematics and Mathematical Sciences

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

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Algebra

Discrete Dynamics in Nature and Society

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Decision SciencesAdvances in

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Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Stochastic AnalysisInternational Journal of

6 Mathematical Problems in Engineering

IC design service

Supplier Customer

Servicecluster

Suppliercluster

Customercluster

Figure 4 The supply chain of IC semiconductor industry

(see Figure 4) The IC design service is one of semiconductormanufacturing processing before launching into IC foundryThe IC design service firms obtain the order from customerand through project management controls the design sched-ule for customer needs More than 365 IC design servicefirms have been established around Hsinchu Science Parkin Taiwan This IC service design firm cluster situation isvery special in the world Consequently studying Taiwanrsquos ICdesign service firms is a very interesting empirical case

42 The Problems of IC Semiconductor Industry CurrentlyTaiwanrsquos semiconductors face a very slow economics situationof business operation Government release encourage policytry to save the semiconductor industry but these policies wasfail The economics of semiconductor continues going downof economic curve The IC industry external environmentalfactors are multiples and dynamics specializing on theIC design service sector Also the IC design service firmrequires one high performance project management to helpmanage related task Integrating internal human resource andleveraging external resource and adjusting business strategyformatch customer needs of IC designmarket As such firmsshould offer just-in-time mechanism of services for productsdelivery and quick response system for customers A little losson service process could bring the result of lose customerTherefore IC design firm should determine to operate atlowest cost and high quality level for IC design service Highperformance IC design service systems should be consideredin the semiconductor supply chain system

43 IC Design Service Firms The IC design skill is one of keytechnical of IC semiconductor manufacturing system It is

a new business model emerging as a vertical disintegrationproduct in the IC semiconductor industry The IC semicon-ductor combines multiprocesses into one complete systemincluding IC design and an IDM (integrated device manu-facturer) fabrication assembly test in the 1970s IC-designsand IDM were separated in the 1980s from one complete setsystem to develop two subsystems of IC design and IDMfabrication and continues developing including IC designand IDM fabrication and assembly The IC semiconductorindustry after the 1980s modified processing to include thesystem IC design IDM fabrication IC foundry IC assemblyand IC test on the subsystem The IC design subsystem after2001 extends from one to three parts including SiP (systemin package) design system and IC design The IC designservice firm has become an important process of the ICsemiconductors industry Figure 5 shows IC design servicedevelopment trajectory from 1960 to 2013

The IC design service firm is a high technology knowl-edge industry The IC design service firm requires pro-fessional human design who must have engineering back-ground specialized design technology knowledge and alsoneed high speed internet networks a convenient communi-cation digital platform and a high operation performanceThis study expects a building of an MCDM model with DeNovo programming for integrating the resource of IC designservice firmsThe IC design service firm is multidimensionaland highly competitive focusing on special professionaltechniques and quick response of design services De Novoprogramming helps to plan service model for achievingaspiration levels of the IC design service firms

44 The Relationship of IC Design Service Supplier andCustomer The IC semiconductors industry is a multipleproduction process that includes design fabrication foundrytesting assembly and delivery of products to customer TheIC design service is a substructure under the IC supplierproviding IC design service for supplier or customer The ICdesign service firms offer system in package (SiP) design todirect customer or IC design and IC layout design for supplier(see Figure 6) The IC design service firms offers value addedat each design service process for example highlightingperformance powerful function low production cost systemon chip (SoC) and redownsizing IC design space Figure 6shows relationship between of IC design service supplier andcustomer

The IC design service firms not only provides designservice for IC semiconductors manufacturing systems butalso offers new innovative ideasfunction on IC chip forcustomer The IC design service firms generally providesprofessional IC design knowledge and IC foundry techniquesthat can obtain greater economics efficiency of semiconduc-tors production the result should be increased IC design ser-vice business opportunity Consequently adopting innovativeideas becomes a very important task in IC design serviceprocessing

45 Through De Novo Programming Achieving AspirationGoal This study expects through De Novo programming to

Mathematical Problems in Engineering 7

After 20001980s to 1990sBefore 1970s

IC design

System IC-ASSP IC-ASIC SOC-IP

SystemSystemSystemSystemSystemIC designIDM fabassembly

test

DS

IC design

IDM fab

Foundry

Assembly

Test

IC design

IDM fab

Foundry

Assembly

Test

IC design

IDM fab

Assembly

SystemIC designIDM fabassembly

test

20001990198019701960 2013

SIP

Figure 5 The IC design service development trajectory from 1960 to 2013 year

IC designservice

Supplier CustomerFinished products

SiP design service

Design requirement IC design service

Order requirement

IC layout order

Figure 6 The relationship of IC design service supplier andcustomer

achieve optimal resource integrating purpose The IC designservice firm must consider resource constrain under mul-tiple objectives alternatives before making decisions Firmface multiple objectives and evaluation criteria of problemnamely dealing with resources integrating and adjustingbusiness strategic to achieve the aspiration goal of businessIn general through trade-off skill it is almost impossible toobtain optimization of all criteria of a given system Zeleny[32] states the new system can eliminate the resource con-strain through redesign then preferably obtain optimizationresults what can improves theTrade-off problems Zeleny [35]proposes the optimal portfolio resource allocation conceptwhich designs a resource integrated system that is individualresource levels cannot determine separately so new designsystems do not have trade-offs to consider Zeleny [33]develops a De Novo programming method for designing an

Low

High

High

For achieving the aspireddesired

level

Good

The best

The best

Efficiencies

Perfo

rman

ce

Figure 7Through De Novo programming achieving the aspirationgoal

optimal system by reshaping the feasible set [33] Firm canmake strategy alliances with partner of supply chain systemand achieves resource integration adjusting the businessgoal remodifying the business model and justifying targetcustomer services This study expects through De Novo pro-gramming approach to compute the data of resource integrat-ing and justify the resource allocationobjective alternativefrom a good level to best level thus achieving aspiration goal(see Figure 7)

8 Mathematical Problems in Engineering

46 Empirical Case Study

461 A Case Study of IC Production Firm A general case ofIC production problem involving two IC products GPS andcell phone of IC in quantities 1199091 and 1199092 each consuming fivedifferent resources (unitmarket prices of resources are given)The data is summarized as shown in Table 1

The costs of the given resources portfolio

(28 times 32) + (35 times 30) + (10 times 55)

+ (18 times 10) + (10 times 28) = $2956(10)

Unit costs of producing one unit goods of the two products

1199091 = (28 lowast 6) + (35 lowast 3) + (10 lowast 14) + (18 lowast 2)

+ (10 lowast 6) = $509

1199092 = (28 lowast 2) + (35 lowast 5) + (10 lowast 6) + (18 lowast 5)

+ (10 lowast 8) = $461

(11)

Expected profit margins (price-cost) arethe profit of 1199091 product = $549 minus $509 = $40unitthe profit of 1199092 product = $491 minus $461 = $30unit

Maximizing total value of function 11989111198911 = 401199091 + 301199092 (12)

Maximizing total quality index 11989121198912 = 81199091 + 101199092 (13)

Maximizing levels of two products calculated by De Novoprogramming

max 1198911 = 401199091 + 301199092

max 1198912 = 81199091 + 101199092

st 61199091 + 21199092 le 32

31199091 + 51199092 le 30

141199091 + 61199092 le 55

21199091 + 51199092 le 10

61199091 + 81199092 le 28

1199091 1199092 ge 0

(14)

Maximum 1198911 in profit

max 1198911 997888rarr 1199091 = 371

1199092 = 052

119891lowast

1= 40 times 371 + 30 times 052 = $164

(15)

Maximum 1198912 in total quality index

max 1198912 997888rarr 1199091 = 371

1199092 = 052

119891lowast

2= 8 times 371 + 10 times 052 = $3488

(16)

Minimizing the total cost by considering the followingconstraints

min 5091199091 + 4611199092

st 401199091 + 301199092 le 164

81199091 + 101199092 le 3488

(17)

Maximum 1198911 in profit

max 1198911 997888rarr 1199091 = 371

1199092 = 052

119891lowast

1= 40 times 371 + 30 times 052 = $164

(18)

Maximum 1198912 in total quality index

max 1198912 997888rarr 1199091 = 371

1199092 = 052

119891lowast

2= 8 times 371 + 10 times 052 = $3488

(19)

Cost of the newly designed system

(28 times 2328) + (35 times 1368) + (10 times 5506)

+ (18 times 1002) + (10 times 2642) = $21258(20)

The new portfolio of resources proposed by the consultant isshown in Table 2

The result of data analysis shows that the new planningsystem cost is $21258

The given resource portfolio cost is $2956 Comparisonbetween original and new planning model we found outthat the new planning model can reduce cost to $8305 andthat new resource planning model is one reliable modelConsequently the IC design service firm can follow the newplanningmodel ofDe novo programming to process resourceintegration

462 The Case of IC Design Service Firm The IC designservice firm is a professional workshop that focuses on theIC design task dependent on what design they can provideservice The IC design service task is also a project-basedplanning under a large RampDproject with business developingstrategy A project-based firm uses external delivery projectsfor business purposes [47 48] This study utilizes a real caseof IC design project management to practice on IC designservice firm Project planning may include many subprojectsthat depend on different cases of customerrsquos demand Centralfeatures of project management were identified according toindividual project uniqueness human resources of projectand business network complexity discontinuity of demandand relationships between other projects and considerablefinancial commitment of the parties [49 50] Conductingaspiration project planning under resource constraint is atarget issue for the IC design service firm This real caseincludes five different resources which included humanpower resource design equipments design materials special

Mathematical Problems in Engineering 9

Table 1 The IC of GPS and cell phone production requiring material

Unit price ($) Material of production processTechnological coefficients

Resource portfolio(units)(Resource requirement)

1199091

1199092

28 Silver wire 6 2 3235 Golden wire 3 5 3010 Silicon Crystal 14 6 5518 Chemistry material 2 5 1010 Conductor material 6 8 28

Table 2 The new resource portfolio planning of IC of GPS and cell phone

Unit price ($) Material of production processTechnological coefficients

Resource portfolio(units)(Resource requirement)

1199091

1199092

28 Silver wire 6 2 232835 Golden wire 3 5 136810 Silicon Crystal 14 6 550618 Chemistry material 2 5 100210 Conductor material 6 8 2642

technical human and design outsourcing with two differentproject teams for the A company customer to performthe IC design task The current study names the twoprojects as the 1198751 and 1198752 project team programs This realplanning program case utilizes the De Novo programmingfor adjustable resource allocation and seeks out minimumcost with maximum performance This work transitions thefive kinds of resources (design equipments design humanpower design material special technical human and design-outsourcing) in the case study to dollar amounts for easy costcalculation Design equipments cost is $380 per unit designhuman power labor cost is $240 per unit design materialresource cost is $200 special technical human cost is $260per unit and design outsourcing cost is $120 per unit

Costs of given resources portfolio

(380 times 26) + (240 times 50) + (200 times 30) + (120 times 10)

+ (260 times 16) = $33240(21)

Unit costs for running each project of the two projects

1198751 = (380 lowast 8) + (240 lowast 10) + (200 lowast 4)

+ (260 lowast 6) = $7800

1198752 = (380 lowast 6) + (240 lowast 4) + (200 lowast 4)

+ (120 lowast 4) = $4520

(22)

Expected profit margins (price cost) are

1198751 = $8200 minus $7800 = $400unit

1198752 = $4820 minus $4520 = $300unit(23)

Maximizing total value of function 1198911

1198911 = 4001199011 + 3001199012 (24)

Maximizing total quality index 1198912

1198912 = 81199011 + 101199012 (25)

Maximizing levels of the two projects calculated by De Novoprogramming

max 1198911 = 4001199011 + 3001199012

max 1198912 = 81199011 + 101199012

st 81199011 + 61199012 le 26

101199011 + 41199012 le 50

41199011 + 41199012 le 30

41199012 le 10

61199011 le 16

1199011 1199012 ge 0

(26)

Maximum 1198911 in profit

max 1198911 997888rarr 1199011 = 267

1199012 = 078

119891lowast

1= 400 times 267 + 300 times 078 = $1300

(27)

10 Mathematical Problems in Engineering

Table 3 1198751and 119875

2project team program require resource of IC design service

Unit price ($) Resources of IC designProject coefficients

Resource portfolio(units)

(Resource requirement)1198751

1198752

380 Design equipments 8 6 26240 Human power resource 10 4 50200 Design material 4 4 30120 Design outsource 0 4 10260 Special technical human 6 0 16

Table 4 The new resource portfolio planning model of 1198751and 119875

2project team program

Unit price ($) Resources of IC designProject coefficients

Resource portfolio(units)(Resource requirement)

1198751

1198752

380 Design equipments 8 6 26240 Human power resource 10 4 325200 Design material 4 4 13120 Design outsource 0 4 0260 Special technical human 6 0 195

Maximum 1198912 in total quality index

max 1198912 997888rarr 1199011 = 267

1199012 = 078

119891lowast

2= 8 times 267 + 10 times 078 = $2916

(28)

Minimizing the total cost by considering the followingconstraints

min 78001199011 + 45201199012

st 1198911 = 4001199011 + 3001199012 le 1300

1198912 = 81199011 + 101199012 le 2916

(29)

Maximum 1198911 in profit

max 1198911 997888rarr 1199011 = 325

1199012 = 0

119891lowast

1= 400 times 325 = $1300

(30)

Maximum 1198912 in total quality index

max 1198912 997888rarr 1199011 = 325

1199012 = 0

119891lowast

2= 8 times 325 = $26

(31)

Cost of new design planning

(380 times 26) + (240 times 325) + (200 times 13) + (260 times 195)

= $25350(32)

New portfolio of resources proposed by the consultant isshown in Table 4

47 Discussions

471 The General Case of IC Production Firm The datashow the empirical case study using the De Novo program-ming which remodifies the planning model and adjusts theresources allocation portfolio The new plan model offersthe best optimized resource integration and reduces losingand enhances firmrsquos competitiveness Technical problemsprogramming are discussed as follows

(i) The original plan needs a total cost of $2956 withgiven resources portfolios

[(28 times 32) + (35 times 30) + (10 times 55) + (18 times 10)

+ (10 times 28) = $2956] (33)

(ii) The new planning model of resource allocation onlyrequires cost of $21258

[(28 times 2328) + (35 times 1368) + (10 times 5506)

+ (18 times 1002) + (10 times 2642) = $21258] (34)

(iii) The newly planning model can save $8302 costConsequently firm seeks new planning model tointegrate resource

$2956 minus $21258 = $8302 (35)

(iv) The newly planning model use the De Novo pro-gramming to reduce resource loss and get betterperformance than the original planning model Thispaper also utilizes the empirical case examination ofDe Novo programming for IC design service firmperformance The new planning model obtains muchbetter resource integration

Mathematical Problems in Engineering 11

472 The Case of IC Design Service Firm Firms have massproduction capacity limitation per one time period due tolimited resources especially for the preceding competitivefacility location problem on the decentralized supply chain[51] The IC design service firm is a standard project-basedprogrammodel Many project teams program through coop-eration under a large design program for different customerare needed This study utilizes a case study of IC design firmfor planning an IC design to customer serviceThis case studyincludes two project teams The 1198751 and 1198752 project with twodifferent plan programming on IC design see (Table 3) Oneproject team leader prefers hiring a special technical engineerfor the IC design task that can maintain the IC design keytechnician and train other staffThe other project team leaderprefers outsourcing design that reduces cost The IC designservice firm chooses the alternative optimal project teambased on the result of De Novo programming

The resource portfolio of firmrsquos original cost is as follows

(380 times 26) + (240 times 50) + (200 times 30) + (120 times 10)

+ (260 times 16) = $33240(36)

The cost of newly planning model through the De Novoprogramming is as follows

(380 times 26) + (240 times 325) + (200 times 13) + (260 times 195)

= $25350(37)

The new planning model saves cost as follows

$33240 minus $25350 = $7890 (38)

The IC design firm adopting1198751 project team cost is as follows

1199011 (8 times 26) + (10 times 325) + (4 times 13) + (6 times 195) = $702(39)

The IC design firm adopting1198752 project team cost is as follows

1199012 (6 times 26) + (4 times 325) + (4 times 13) = $338 (40)

The result of data analysis shows through the De Novoprogramming method the IC design service firm that canfind out which project team is the optimal solution In thisempirical case adopting 1198752 project team can save design costmuchmore than1198751 projectThe new planningmodel reduces$7890 in cost compared with the original planning modelSo the newly planning model is the best choice for achievingaspiration goal

5 Conclusion

The IC semiconductor industry faces uncertainty anddynamic external environment especially at IC design servicefirm that needs one resource integrating programmingmodelfor new product development [52] Adopting an efficientstrategic alliancesresource integrating planning model is akey issue for achieving firmrsquos business goals especially astheir resource was constrainedThe present study considers a

planningmodel based on resource constraintsThis study uti-lizes the De Novo programming for achieving firmrsquos strategicgoal The De Novo programming can help firms find out anoptimal resource integration base on firm financial considerThis planning model provided reliable systematize approachthat combines alliances from external resources and changethem to internal resources application The project team1198752 adopting open innovation concept through outsourcingreduces design cost and enhances design performance DeNovo programming not only obtains optimal resource inte-gration but also promotes the conceptual of strategic alliancesfor firmrsquos cooperation between each other

51 Management Implication This research aims to create aresource integrating plan model through IC design servicefirm as an empirical case for examining the result of theplanning model The planning model is based on openinnovation theory exploring the IC design service firmwhichintegrates IC design resources and makes optimal resourcesintegrate in IC design service firm The planning model ofDe Novo programming is not only for IC design service firmbut also can apply to the other industrial implementationstrategic allianceresource integration This planning modelis a universal model for the other industry fields

52 Suggestion The IC design service industry is a veryspecial industry We suggest the extend to the other industrythat can find out different results Suggestion utilizes the otherapproach of MCDMmethod exploring different case studiesof industry

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

The authors appreciate two blind reviewers who have pro-vided very excellent comments These comments are verygood for the authors to improve the quality of the paperAlso thanks to the editor Professor Chang for offering lots ofexcellent suggestions that helped enhance this paperrsquos quality

References

[1] R Varadarajan ldquoOutsourcing think more expansivelyrdquo Journalof Business Research vol 62 no 11 pp 1165ndash1172 2009

[2] G Forgionne and Z Guo ldquoInternal supply chain coordinationin the electric utility industryrdquo European Journal of OperationalResearch vol 196 no 2 pp 619ndash627 2009

[3] R George and R Kabir ldquoBusiness groups and profit redistribu-tion a boon or bane for firmsrdquo Journal of Business Research vol61 no 9 pp 1004ndash1014 2008

[4] L Duckstein and S Opricovic ldquoMultiobjective optimization inriver basin developmentrdquoWater Resources Research vol 16 no1 pp 14ndash20 1980

12 Mathematical Problems in Engineering

[5] M Zeleny ldquoA case study in multiple objective design DeNovo programmingrdquo inMultiple Criteria Analysis OperationalMethods vol 1 pp 37ndash52 1981

[6] M Zeleny ldquoOptimal system design with multiple criteria DeNovo programming approachrdquo Engineering Costs and Produc-tion Economics vol 10 no 1 pp 89ndash94 1986

[7] S Christophersona M Kitsonb and J Michiec ldquoInnovationnetworks and knowledge exchangerdquo Cambridge Journal ofRegions Economy and Society vol 1 no 2 pp 165ndash173 2008

[8] H W Chesbrough Open Innovation The New Imperativefor Creating and Profiting from Technology Harvard BusinessSchool Press Boston Mass USA 2003

[9] J B Barney ldquoFirm resources and sustained competitive advan-tagerdquo Advances in Strategic Management vol 17 pp 203ndash2272000

[10] B Wernerfelt ldquoA resource-based view of the firmrdquo StrategicManagement Journal vol 5 no 2 pp 171ndash180 1984

[11] T R Holcomb and M A Hitt ldquoToward a model of strategicoutsourcingrdquo Journal of Operations Management vol 25 no 2pp 464ndash481 2007

[12] D J Collis ldquoA resource-based analysis of global competitionthe case of the bearings industryrdquo Strategic Management Jour-nal vol 12 no 1 pp 49ndash68 1991

[13] E Penrose The Theory of the Growth of the Firm John Wileyand Sons New York NY USA 1959

[14] E Anderson andH Gatignon ldquoModes of foreign entry a trans-action cost analysis and propositionsrdquo Journal of InternationalBusiness Studies vol 17 no 3 pp 1ndash26 1986

[15] J Chimhanzi and R E Morgan ldquoExplanations from themarketinghuman resources dyad formarketing strategy imple-mentation effectiveness in service firmsrdquo Journal of BusinessResearch vol 58 no 6 pp 787ndash796 2005

[16] J Fahy ldquoA resource-based analysis of sustainable competitiveadvantage in a global environmentrdquo International BusinessReview vol 11 no 1 pp 57ndash78 2002

[17] M A Harrison R E Hoskisson and D Ireland ldquoSynergies andpost acquisition performance differences versus similarities inresource allocationsrdquo Journal of Management vol 17 no 1 pp173ndash190 1991

[18] D J Teece G Pisano and A Shuen ldquoDynamic capabilities andstrategic managementrdquo Strategic Management Journal vol 18no 7 pp 509ndash533 1997

[19] V A Varma J F Pekny G E Blau and G V Reklaitis ldquoAframework for addressing stochastic and combinatorial aspectsof scheduling and resource allocation in pharmaceutical RampDpipelinesrdquoComputers andChemical Engineering vol 32 no 4-5pp 1000ndash1015 2008

[20] R Kolisch ldquoEfficient priority rules for the resource-constrainedproject scheduling problemrdquo Journal of Operations Manage-ment vol 14 no 3 pp 179ndash192 1996

[21] R Kolisch and S Hartmann ldquoExperimental investigationof heuristics for resource-constrained project scheduling anupdaterdquo European Journal of Operational Research vol 174 no1 pp 23ndash37 2006

[22] M J Leiblein ldquoThe choice of organizational governance formand performance predictions from transaction cost resource-based and real options theoriesrdquo Journal of Management vol29 no 6 pp 937ndash961 2003

[23] G CMoore and I Benbasat ldquoDevelopment of an instrument tomeasure the perceptions of adopting an information technologyinnovationrdquo Information Systems Research vol 2 no 3 pp 192ndash222 1991

[24] R E Hoskisson M A Hitt W P Wan and D Yiu ldquoTheoryand research in strategic management swings of a pendulumrdquoJournal of Management vol 25 no 3 pp 417ndash456 1999

[25] C C Miller and L B Cardinal ldquoStrategic planning and firmperformance a synthesis of more than two decades of researchrdquoAcademy of Management Journal vol 37 no 6 pp 1649ndash16651994

[26] S D Hunt A General Theory of Competition Sage ThousandOaks Calif USA 2000

[27] S Hart and C Banbury ldquoHow strategy-making processes canmake a differencerdquo Strategic Management Journal vol 15 no 4pp 251ndash269 1994

[28] E Drakopoulos ldquoEnterprise network planning and designmethodology and applicationrdquo Computer Communications vol22 no 4 pp 340ndash352 1999

[29] V Kumar B Maheshwari and U Kumar ldquoERP systems imple-mentation best practices in Canadian government organiza-tionsrdquoGovernment InformationQuarterly vol 19 no 2 pp 147ndash172 2002

[30] W R Lewis ldquoStrategic planningrdquo Hospital Materiel Manage-ment Quarterly vol 10 no 4 pp 57ndash63 1989

[31] D Wainwright and T Waring ldquoThree domains for imple-menting integrated information systems redressing the bal-ance between technology strategic and organisational analysisrdquoInternational Journal of InformationManagement vol 24 no 4pp 329ndash346 2004

[32] M Zeleny ldquoOptimal system design with multiple criteria DeNovo programming approachrdquo Engineering Costs and Produc-tion Economics vol 10 no 1 pp 89ndash95 1986

[33] M Zeleny ldquoOptimal given system versus designing optimalsystem the De Novo programming approachrdquo InternationalJournal of General System vol 17 no 3 pp 295ndash307 1990

[34] Y M Zhang G H Huang and X D Zhang ldquoInexact de Novoprogramming for water resources systems planningrdquo EuropeanJournal ofOperational Research vol 199 no 2 pp 531ndash541 2009

[35] M Zeleny ldquoTrade-offs free management via De Novo pro-grammingrdquo International Journal Operations and QuantitativeManagement vol 1 no 1 pp 3ndash13 1995

[36] H Chesbrough W Anhaverbeke and J West Eds Openinnovation Researching a New Paradigm Oxford UniversityPress 2006

[37] J West and S Gallagher ldquoChallenges of open innovation theparadox of firm investment in open-source softwarerdquo R amp DManagement vol 36 no 3 pp 319ndash331 2006

[38] E H Kessler P E Bierly and S Gopalakrishnan ldquoInternalversus external learning in new product development effects onspeed costs and competitive advantagerdquo R amp D Managementvol 30 no 3 pp 213ndash223 2000

[39] O Gassmann and E Enkel ldquoTowards a theory of open innova-tion three core process archetypesrdquo in Proceedings of the RampDManagement Conference (RADMA rsquo04) Lisbon Portugal 2004

[40] L V Kantorovich and T C Koopmans Problems of Applicationof Optimization Methods in Industry Federation of SwedishIndustry 1976

[41] T K Das and B-S Teng ldquoA resource-based theory of strategicalliancesrdquo Journal of Management vol 26 no 1 pp 31ndash61 2000

[42] Z Babic and I Pavic ldquoMulticriterial production planning byDe Novo programming approachrdquo International Journal ofProduction Economics vol 43 no 1 pp 59ndash66 1996

[43] J K C Chen and G H Tzeng ldquoPerspective strategic allianceand resource allocation in supply chain systems through the De

Mathematical Problems in Engineering 13

Novo programming approachrdquo International Journal Sustain-able Strategic Management vol 1 no 3 pp 320ndash339 2009

[44] J-J Huang G-H Tzeng and C-S Ong ldquoMotivation andresource-allocation for strategic alliances through the De NovoperspectiverdquoMathematical and ComputerModelling vol 41 no6-7 pp 711ndash721 2005

[45] J-J Huang G-H Tzeng and C-S Ong ldquoChoosing bestalliance partners and allocating optimal alliance resources usingthe fuzzymulti-objective dummyprogrammingmodelrdquo Journalof the Operational Research Society vol 57 no 10 pp 1216ndash12232006

[46] R M Grant ldquoThe resource-based theory competitive advan-tage implications for strategy formulationrdquo California Manage-ment Review vol 33 no 3 pp 114ndash135 1991

[47] K A Artto and K Wikstrom ldquoWhat is project businessrdquoInternational Journal of Project Management vol 23 no 5 pp343ndash353 2005

[48] J Soderlund ldquoOn the broadening scope of the research onprojects a review and a model for analysisrdquo InternationalJournal of ProjectManagement vol 22 no 8 pp 655ndash667 2004

[49] B Cova P Ghauri and R Salle Project Marketing BeyondCompetitive Bidding JohnWiley amp Sons Chichester UK 2002

[50] T Mandjak and Z Veres ldquoThe D-U-C model and the stages ofthe project marketing processrdquo in Proceedings of the 14th IMPAnnual Conference Proceedings H K Nummela Ed pp 471ndash490 1998

[51] Q Meng Y Huang and R L Cheu ldquoCompetitive facilitylocation on decentralized supply chainsrdquo European Journal ofOperational Research vol 196 no 2 pp 487ndash499 2009

[52] H-M Hsu and W-P Wang ldquoDynamic programming fordelayed product differentiationrdquo European Journal of Opera-tional Research vol 156 no 1 pp 183ndash193 2004

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Mathematical Problems in Engineering

Hindawi Publishing Corporationhttpwwwhindawicom

Differential EquationsInternational Journal of

Volume 2014

Applied MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Mathematical PhysicsAdvances in

Complex AnalysisJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

OptimizationJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Operations ResearchAdvances in

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Function Spaces

Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of Mathematics and Mathematical Sciences

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Algebra

Discrete Dynamics in Nature and Society

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Decision SciencesAdvances in

Discrete MathematicsJournal of

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Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Stochastic AnalysisInternational Journal of

Mathematical Problems in Engineering 7

After 20001980s to 1990sBefore 1970s

IC design

System IC-ASSP IC-ASIC SOC-IP

SystemSystemSystemSystemSystemIC designIDM fabassembly

test

DS

IC design

IDM fab

Foundry

Assembly

Test

IC design

IDM fab

Foundry

Assembly

Test

IC design

IDM fab

Assembly

SystemIC designIDM fabassembly

test

20001990198019701960 2013

SIP

Figure 5 The IC design service development trajectory from 1960 to 2013 year

IC designservice

Supplier CustomerFinished products

SiP design service

Design requirement IC design service

Order requirement

IC layout order

Figure 6 The relationship of IC design service supplier andcustomer

achieve optimal resource integrating purpose The IC designservice firm must consider resource constrain under mul-tiple objectives alternatives before making decisions Firmface multiple objectives and evaluation criteria of problemnamely dealing with resources integrating and adjustingbusiness strategic to achieve the aspiration goal of businessIn general through trade-off skill it is almost impossible toobtain optimization of all criteria of a given system Zeleny[32] states the new system can eliminate the resource con-strain through redesign then preferably obtain optimizationresults what can improves theTrade-off problems Zeleny [35]proposes the optimal portfolio resource allocation conceptwhich designs a resource integrated system that is individualresource levels cannot determine separately so new designsystems do not have trade-offs to consider Zeleny [33]develops a De Novo programming method for designing an

Low

High

High

For achieving the aspireddesired

level

Good

The best

The best

Efficiencies

Perfo

rman

ce

Figure 7Through De Novo programming achieving the aspirationgoal

optimal system by reshaping the feasible set [33] Firm canmake strategy alliances with partner of supply chain systemand achieves resource integration adjusting the businessgoal remodifying the business model and justifying targetcustomer services This study expects through De Novo pro-gramming approach to compute the data of resource integrat-ing and justify the resource allocationobjective alternativefrom a good level to best level thus achieving aspiration goal(see Figure 7)

8 Mathematical Problems in Engineering

46 Empirical Case Study

461 A Case Study of IC Production Firm A general case ofIC production problem involving two IC products GPS andcell phone of IC in quantities 1199091 and 1199092 each consuming fivedifferent resources (unitmarket prices of resources are given)The data is summarized as shown in Table 1

The costs of the given resources portfolio

(28 times 32) + (35 times 30) + (10 times 55)

+ (18 times 10) + (10 times 28) = $2956(10)

Unit costs of producing one unit goods of the two products

1199091 = (28 lowast 6) + (35 lowast 3) + (10 lowast 14) + (18 lowast 2)

+ (10 lowast 6) = $509

1199092 = (28 lowast 2) + (35 lowast 5) + (10 lowast 6) + (18 lowast 5)

+ (10 lowast 8) = $461

(11)

Expected profit margins (price-cost) arethe profit of 1199091 product = $549 minus $509 = $40unitthe profit of 1199092 product = $491 minus $461 = $30unit

Maximizing total value of function 11989111198911 = 401199091 + 301199092 (12)

Maximizing total quality index 11989121198912 = 81199091 + 101199092 (13)

Maximizing levels of two products calculated by De Novoprogramming

max 1198911 = 401199091 + 301199092

max 1198912 = 81199091 + 101199092

st 61199091 + 21199092 le 32

31199091 + 51199092 le 30

141199091 + 61199092 le 55

21199091 + 51199092 le 10

61199091 + 81199092 le 28

1199091 1199092 ge 0

(14)

Maximum 1198911 in profit

max 1198911 997888rarr 1199091 = 371

1199092 = 052

119891lowast

1= 40 times 371 + 30 times 052 = $164

(15)

Maximum 1198912 in total quality index

max 1198912 997888rarr 1199091 = 371

1199092 = 052

119891lowast

2= 8 times 371 + 10 times 052 = $3488

(16)

Minimizing the total cost by considering the followingconstraints

min 5091199091 + 4611199092

st 401199091 + 301199092 le 164

81199091 + 101199092 le 3488

(17)

Maximum 1198911 in profit

max 1198911 997888rarr 1199091 = 371

1199092 = 052

119891lowast

1= 40 times 371 + 30 times 052 = $164

(18)

Maximum 1198912 in total quality index

max 1198912 997888rarr 1199091 = 371

1199092 = 052

119891lowast

2= 8 times 371 + 10 times 052 = $3488

(19)

Cost of the newly designed system

(28 times 2328) + (35 times 1368) + (10 times 5506)

+ (18 times 1002) + (10 times 2642) = $21258(20)

The new portfolio of resources proposed by the consultant isshown in Table 2

The result of data analysis shows that the new planningsystem cost is $21258

The given resource portfolio cost is $2956 Comparisonbetween original and new planning model we found outthat the new planning model can reduce cost to $8305 andthat new resource planning model is one reliable modelConsequently the IC design service firm can follow the newplanningmodel ofDe novo programming to process resourceintegration

462 The Case of IC Design Service Firm The IC designservice firm is a professional workshop that focuses on theIC design task dependent on what design they can provideservice The IC design service task is also a project-basedplanning under a large RampDproject with business developingstrategy A project-based firm uses external delivery projectsfor business purposes [47 48] This study utilizes a real caseof IC design project management to practice on IC designservice firm Project planning may include many subprojectsthat depend on different cases of customerrsquos demand Centralfeatures of project management were identified according toindividual project uniqueness human resources of projectand business network complexity discontinuity of demandand relationships between other projects and considerablefinancial commitment of the parties [49 50] Conductingaspiration project planning under resource constraint is atarget issue for the IC design service firm This real caseincludes five different resources which included humanpower resource design equipments design materials special

Mathematical Problems in Engineering 9

Table 1 The IC of GPS and cell phone production requiring material

Unit price ($) Material of production processTechnological coefficients

Resource portfolio(units)(Resource requirement)

1199091

1199092

28 Silver wire 6 2 3235 Golden wire 3 5 3010 Silicon Crystal 14 6 5518 Chemistry material 2 5 1010 Conductor material 6 8 28

Table 2 The new resource portfolio planning of IC of GPS and cell phone

Unit price ($) Material of production processTechnological coefficients

Resource portfolio(units)(Resource requirement)

1199091

1199092

28 Silver wire 6 2 232835 Golden wire 3 5 136810 Silicon Crystal 14 6 550618 Chemistry material 2 5 100210 Conductor material 6 8 2642

technical human and design outsourcing with two differentproject teams for the A company customer to performthe IC design task The current study names the twoprojects as the 1198751 and 1198752 project team programs This realplanning program case utilizes the De Novo programmingfor adjustable resource allocation and seeks out minimumcost with maximum performance This work transitions thefive kinds of resources (design equipments design humanpower design material special technical human and design-outsourcing) in the case study to dollar amounts for easy costcalculation Design equipments cost is $380 per unit designhuman power labor cost is $240 per unit design materialresource cost is $200 special technical human cost is $260per unit and design outsourcing cost is $120 per unit

Costs of given resources portfolio

(380 times 26) + (240 times 50) + (200 times 30) + (120 times 10)

+ (260 times 16) = $33240(21)

Unit costs for running each project of the two projects

1198751 = (380 lowast 8) + (240 lowast 10) + (200 lowast 4)

+ (260 lowast 6) = $7800

1198752 = (380 lowast 6) + (240 lowast 4) + (200 lowast 4)

+ (120 lowast 4) = $4520

(22)

Expected profit margins (price cost) are

1198751 = $8200 minus $7800 = $400unit

1198752 = $4820 minus $4520 = $300unit(23)

Maximizing total value of function 1198911

1198911 = 4001199011 + 3001199012 (24)

Maximizing total quality index 1198912

1198912 = 81199011 + 101199012 (25)

Maximizing levels of the two projects calculated by De Novoprogramming

max 1198911 = 4001199011 + 3001199012

max 1198912 = 81199011 + 101199012

st 81199011 + 61199012 le 26

101199011 + 41199012 le 50

41199011 + 41199012 le 30

41199012 le 10

61199011 le 16

1199011 1199012 ge 0

(26)

Maximum 1198911 in profit

max 1198911 997888rarr 1199011 = 267

1199012 = 078

119891lowast

1= 400 times 267 + 300 times 078 = $1300

(27)

10 Mathematical Problems in Engineering

Table 3 1198751and 119875

2project team program require resource of IC design service

Unit price ($) Resources of IC designProject coefficients

Resource portfolio(units)

(Resource requirement)1198751

1198752

380 Design equipments 8 6 26240 Human power resource 10 4 50200 Design material 4 4 30120 Design outsource 0 4 10260 Special technical human 6 0 16

Table 4 The new resource portfolio planning model of 1198751and 119875

2project team program

Unit price ($) Resources of IC designProject coefficients

Resource portfolio(units)(Resource requirement)

1198751

1198752

380 Design equipments 8 6 26240 Human power resource 10 4 325200 Design material 4 4 13120 Design outsource 0 4 0260 Special technical human 6 0 195

Maximum 1198912 in total quality index

max 1198912 997888rarr 1199011 = 267

1199012 = 078

119891lowast

2= 8 times 267 + 10 times 078 = $2916

(28)

Minimizing the total cost by considering the followingconstraints

min 78001199011 + 45201199012

st 1198911 = 4001199011 + 3001199012 le 1300

1198912 = 81199011 + 101199012 le 2916

(29)

Maximum 1198911 in profit

max 1198911 997888rarr 1199011 = 325

1199012 = 0

119891lowast

1= 400 times 325 = $1300

(30)

Maximum 1198912 in total quality index

max 1198912 997888rarr 1199011 = 325

1199012 = 0

119891lowast

2= 8 times 325 = $26

(31)

Cost of new design planning

(380 times 26) + (240 times 325) + (200 times 13) + (260 times 195)

= $25350(32)

New portfolio of resources proposed by the consultant isshown in Table 4

47 Discussions

471 The General Case of IC Production Firm The datashow the empirical case study using the De Novo program-ming which remodifies the planning model and adjusts theresources allocation portfolio The new plan model offersthe best optimized resource integration and reduces losingand enhances firmrsquos competitiveness Technical problemsprogramming are discussed as follows

(i) The original plan needs a total cost of $2956 withgiven resources portfolios

[(28 times 32) + (35 times 30) + (10 times 55) + (18 times 10)

+ (10 times 28) = $2956] (33)

(ii) The new planning model of resource allocation onlyrequires cost of $21258

[(28 times 2328) + (35 times 1368) + (10 times 5506)

+ (18 times 1002) + (10 times 2642) = $21258] (34)

(iii) The newly planning model can save $8302 costConsequently firm seeks new planning model tointegrate resource

$2956 minus $21258 = $8302 (35)

(iv) The newly planning model use the De Novo pro-gramming to reduce resource loss and get betterperformance than the original planning model Thispaper also utilizes the empirical case examination ofDe Novo programming for IC design service firmperformance The new planning model obtains muchbetter resource integration

Mathematical Problems in Engineering 11

472 The Case of IC Design Service Firm Firms have massproduction capacity limitation per one time period due tolimited resources especially for the preceding competitivefacility location problem on the decentralized supply chain[51] The IC design service firm is a standard project-basedprogrammodel Many project teams program through coop-eration under a large design program for different customerare needed This study utilizes a case study of IC design firmfor planning an IC design to customer serviceThis case studyincludes two project teams The 1198751 and 1198752 project with twodifferent plan programming on IC design see (Table 3) Oneproject team leader prefers hiring a special technical engineerfor the IC design task that can maintain the IC design keytechnician and train other staffThe other project team leaderprefers outsourcing design that reduces cost The IC designservice firm chooses the alternative optimal project teambased on the result of De Novo programming

The resource portfolio of firmrsquos original cost is as follows

(380 times 26) + (240 times 50) + (200 times 30) + (120 times 10)

+ (260 times 16) = $33240(36)

The cost of newly planning model through the De Novoprogramming is as follows

(380 times 26) + (240 times 325) + (200 times 13) + (260 times 195)

= $25350(37)

The new planning model saves cost as follows

$33240 minus $25350 = $7890 (38)

The IC design firm adopting1198751 project team cost is as follows

1199011 (8 times 26) + (10 times 325) + (4 times 13) + (6 times 195) = $702(39)

The IC design firm adopting1198752 project team cost is as follows

1199012 (6 times 26) + (4 times 325) + (4 times 13) = $338 (40)

The result of data analysis shows through the De Novoprogramming method the IC design service firm that canfind out which project team is the optimal solution In thisempirical case adopting 1198752 project team can save design costmuchmore than1198751 projectThe new planningmodel reduces$7890 in cost compared with the original planning modelSo the newly planning model is the best choice for achievingaspiration goal

5 Conclusion

The IC semiconductor industry faces uncertainty anddynamic external environment especially at IC design servicefirm that needs one resource integrating programmingmodelfor new product development [52] Adopting an efficientstrategic alliancesresource integrating planning model is akey issue for achieving firmrsquos business goals especially astheir resource was constrainedThe present study considers a

planningmodel based on resource constraintsThis study uti-lizes the De Novo programming for achieving firmrsquos strategicgoal The De Novo programming can help firms find out anoptimal resource integration base on firm financial considerThis planning model provided reliable systematize approachthat combines alliances from external resources and changethem to internal resources application The project team1198752 adopting open innovation concept through outsourcingreduces design cost and enhances design performance DeNovo programming not only obtains optimal resource inte-gration but also promotes the conceptual of strategic alliancesfor firmrsquos cooperation between each other

51 Management Implication This research aims to create aresource integrating plan model through IC design servicefirm as an empirical case for examining the result of theplanning model The planning model is based on openinnovation theory exploring the IC design service firmwhichintegrates IC design resources and makes optimal resourcesintegrate in IC design service firm The planning model ofDe Novo programming is not only for IC design service firmbut also can apply to the other industrial implementationstrategic allianceresource integration This planning modelis a universal model for the other industry fields

52 Suggestion The IC design service industry is a veryspecial industry We suggest the extend to the other industrythat can find out different results Suggestion utilizes the otherapproach of MCDMmethod exploring different case studiesof industry

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

The authors appreciate two blind reviewers who have pro-vided very excellent comments These comments are verygood for the authors to improve the quality of the paperAlso thanks to the editor Professor Chang for offering lots ofexcellent suggestions that helped enhance this paperrsquos quality

References

[1] R Varadarajan ldquoOutsourcing think more expansivelyrdquo Journalof Business Research vol 62 no 11 pp 1165ndash1172 2009

[2] G Forgionne and Z Guo ldquoInternal supply chain coordinationin the electric utility industryrdquo European Journal of OperationalResearch vol 196 no 2 pp 619ndash627 2009

[3] R George and R Kabir ldquoBusiness groups and profit redistribu-tion a boon or bane for firmsrdquo Journal of Business Research vol61 no 9 pp 1004ndash1014 2008

[4] L Duckstein and S Opricovic ldquoMultiobjective optimization inriver basin developmentrdquoWater Resources Research vol 16 no1 pp 14ndash20 1980

12 Mathematical Problems in Engineering

[5] M Zeleny ldquoA case study in multiple objective design DeNovo programmingrdquo inMultiple Criteria Analysis OperationalMethods vol 1 pp 37ndash52 1981

[6] M Zeleny ldquoOptimal system design with multiple criteria DeNovo programming approachrdquo Engineering Costs and Produc-tion Economics vol 10 no 1 pp 89ndash94 1986

[7] S Christophersona M Kitsonb and J Michiec ldquoInnovationnetworks and knowledge exchangerdquo Cambridge Journal ofRegions Economy and Society vol 1 no 2 pp 165ndash173 2008

[8] H W Chesbrough Open Innovation The New Imperativefor Creating and Profiting from Technology Harvard BusinessSchool Press Boston Mass USA 2003

[9] J B Barney ldquoFirm resources and sustained competitive advan-tagerdquo Advances in Strategic Management vol 17 pp 203ndash2272000

[10] B Wernerfelt ldquoA resource-based view of the firmrdquo StrategicManagement Journal vol 5 no 2 pp 171ndash180 1984

[11] T R Holcomb and M A Hitt ldquoToward a model of strategicoutsourcingrdquo Journal of Operations Management vol 25 no 2pp 464ndash481 2007

[12] D J Collis ldquoA resource-based analysis of global competitionthe case of the bearings industryrdquo Strategic Management Jour-nal vol 12 no 1 pp 49ndash68 1991

[13] E Penrose The Theory of the Growth of the Firm John Wileyand Sons New York NY USA 1959

[14] E Anderson andH Gatignon ldquoModes of foreign entry a trans-action cost analysis and propositionsrdquo Journal of InternationalBusiness Studies vol 17 no 3 pp 1ndash26 1986

[15] J Chimhanzi and R E Morgan ldquoExplanations from themarketinghuman resources dyad formarketing strategy imple-mentation effectiveness in service firmsrdquo Journal of BusinessResearch vol 58 no 6 pp 787ndash796 2005

[16] J Fahy ldquoA resource-based analysis of sustainable competitiveadvantage in a global environmentrdquo International BusinessReview vol 11 no 1 pp 57ndash78 2002

[17] M A Harrison R E Hoskisson and D Ireland ldquoSynergies andpost acquisition performance differences versus similarities inresource allocationsrdquo Journal of Management vol 17 no 1 pp173ndash190 1991

[18] D J Teece G Pisano and A Shuen ldquoDynamic capabilities andstrategic managementrdquo Strategic Management Journal vol 18no 7 pp 509ndash533 1997

[19] V A Varma J F Pekny G E Blau and G V Reklaitis ldquoAframework for addressing stochastic and combinatorial aspectsof scheduling and resource allocation in pharmaceutical RampDpipelinesrdquoComputers andChemical Engineering vol 32 no 4-5pp 1000ndash1015 2008

[20] R Kolisch ldquoEfficient priority rules for the resource-constrainedproject scheduling problemrdquo Journal of Operations Manage-ment vol 14 no 3 pp 179ndash192 1996

[21] R Kolisch and S Hartmann ldquoExperimental investigationof heuristics for resource-constrained project scheduling anupdaterdquo European Journal of Operational Research vol 174 no1 pp 23ndash37 2006

[22] M J Leiblein ldquoThe choice of organizational governance formand performance predictions from transaction cost resource-based and real options theoriesrdquo Journal of Management vol29 no 6 pp 937ndash961 2003

[23] G CMoore and I Benbasat ldquoDevelopment of an instrument tomeasure the perceptions of adopting an information technologyinnovationrdquo Information Systems Research vol 2 no 3 pp 192ndash222 1991

[24] R E Hoskisson M A Hitt W P Wan and D Yiu ldquoTheoryand research in strategic management swings of a pendulumrdquoJournal of Management vol 25 no 3 pp 417ndash456 1999

[25] C C Miller and L B Cardinal ldquoStrategic planning and firmperformance a synthesis of more than two decades of researchrdquoAcademy of Management Journal vol 37 no 6 pp 1649ndash16651994

[26] S D Hunt A General Theory of Competition Sage ThousandOaks Calif USA 2000

[27] S Hart and C Banbury ldquoHow strategy-making processes canmake a differencerdquo Strategic Management Journal vol 15 no 4pp 251ndash269 1994

[28] E Drakopoulos ldquoEnterprise network planning and designmethodology and applicationrdquo Computer Communications vol22 no 4 pp 340ndash352 1999

[29] V Kumar B Maheshwari and U Kumar ldquoERP systems imple-mentation best practices in Canadian government organiza-tionsrdquoGovernment InformationQuarterly vol 19 no 2 pp 147ndash172 2002

[30] W R Lewis ldquoStrategic planningrdquo Hospital Materiel Manage-ment Quarterly vol 10 no 4 pp 57ndash63 1989

[31] D Wainwright and T Waring ldquoThree domains for imple-menting integrated information systems redressing the bal-ance between technology strategic and organisational analysisrdquoInternational Journal of InformationManagement vol 24 no 4pp 329ndash346 2004

[32] M Zeleny ldquoOptimal system design with multiple criteria DeNovo programming approachrdquo Engineering Costs and Produc-tion Economics vol 10 no 1 pp 89ndash95 1986

[33] M Zeleny ldquoOptimal given system versus designing optimalsystem the De Novo programming approachrdquo InternationalJournal of General System vol 17 no 3 pp 295ndash307 1990

[34] Y M Zhang G H Huang and X D Zhang ldquoInexact de Novoprogramming for water resources systems planningrdquo EuropeanJournal ofOperational Research vol 199 no 2 pp 531ndash541 2009

[35] M Zeleny ldquoTrade-offs free management via De Novo pro-grammingrdquo International Journal Operations and QuantitativeManagement vol 1 no 1 pp 3ndash13 1995

[36] H Chesbrough W Anhaverbeke and J West Eds Openinnovation Researching a New Paradigm Oxford UniversityPress 2006

[37] J West and S Gallagher ldquoChallenges of open innovation theparadox of firm investment in open-source softwarerdquo R amp DManagement vol 36 no 3 pp 319ndash331 2006

[38] E H Kessler P E Bierly and S Gopalakrishnan ldquoInternalversus external learning in new product development effects onspeed costs and competitive advantagerdquo R amp D Managementvol 30 no 3 pp 213ndash223 2000

[39] O Gassmann and E Enkel ldquoTowards a theory of open innova-tion three core process archetypesrdquo in Proceedings of the RampDManagement Conference (RADMA rsquo04) Lisbon Portugal 2004

[40] L V Kantorovich and T C Koopmans Problems of Applicationof Optimization Methods in Industry Federation of SwedishIndustry 1976

[41] T K Das and B-S Teng ldquoA resource-based theory of strategicalliancesrdquo Journal of Management vol 26 no 1 pp 31ndash61 2000

[42] Z Babic and I Pavic ldquoMulticriterial production planning byDe Novo programming approachrdquo International Journal ofProduction Economics vol 43 no 1 pp 59ndash66 1996

[43] J K C Chen and G H Tzeng ldquoPerspective strategic allianceand resource allocation in supply chain systems through the De

Mathematical Problems in Engineering 13

Novo programming approachrdquo International Journal Sustain-able Strategic Management vol 1 no 3 pp 320ndash339 2009

[44] J-J Huang G-H Tzeng and C-S Ong ldquoMotivation andresource-allocation for strategic alliances through the De NovoperspectiverdquoMathematical and ComputerModelling vol 41 no6-7 pp 711ndash721 2005

[45] J-J Huang G-H Tzeng and C-S Ong ldquoChoosing bestalliance partners and allocating optimal alliance resources usingthe fuzzymulti-objective dummyprogrammingmodelrdquo Journalof the Operational Research Society vol 57 no 10 pp 1216ndash12232006

[46] R M Grant ldquoThe resource-based theory competitive advan-tage implications for strategy formulationrdquo California Manage-ment Review vol 33 no 3 pp 114ndash135 1991

[47] K A Artto and K Wikstrom ldquoWhat is project businessrdquoInternational Journal of Project Management vol 23 no 5 pp343ndash353 2005

[48] J Soderlund ldquoOn the broadening scope of the research onprojects a review and a model for analysisrdquo InternationalJournal of ProjectManagement vol 22 no 8 pp 655ndash667 2004

[49] B Cova P Ghauri and R Salle Project Marketing BeyondCompetitive Bidding JohnWiley amp Sons Chichester UK 2002

[50] T Mandjak and Z Veres ldquoThe D-U-C model and the stages ofthe project marketing processrdquo in Proceedings of the 14th IMPAnnual Conference Proceedings H K Nummela Ed pp 471ndash490 1998

[51] Q Meng Y Huang and R L Cheu ldquoCompetitive facilitylocation on decentralized supply chainsrdquo European Journal ofOperational Research vol 196 no 2 pp 487ndash499 2009

[52] H-M Hsu and W-P Wang ldquoDynamic programming fordelayed product differentiationrdquo European Journal of Opera-tional Research vol 156 no 1 pp 183ndash193 2004

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Mathematical Problems in Engineering

Hindawi Publishing Corporationhttpwwwhindawicom

Differential EquationsInternational Journal of

Volume 2014

Applied MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Mathematical PhysicsAdvances in

Complex AnalysisJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

OptimizationJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Operations ResearchAdvances in

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Function Spaces

Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of Mathematics and Mathematical Sciences

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Algebra

Discrete Dynamics in Nature and Society

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Decision SciencesAdvances in

Discrete MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom

Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Stochastic AnalysisInternational Journal of

8 Mathematical Problems in Engineering

46 Empirical Case Study

461 A Case Study of IC Production Firm A general case ofIC production problem involving two IC products GPS andcell phone of IC in quantities 1199091 and 1199092 each consuming fivedifferent resources (unitmarket prices of resources are given)The data is summarized as shown in Table 1

The costs of the given resources portfolio

(28 times 32) + (35 times 30) + (10 times 55)

+ (18 times 10) + (10 times 28) = $2956(10)

Unit costs of producing one unit goods of the two products

1199091 = (28 lowast 6) + (35 lowast 3) + (10 lowast 14) + (18 lowast 2)

+ (10 lowast 6) = $509

1199092 = (28 lowast 2) + (35 lowast 5) + (10 lowast 6) + (18 lowast 5)

+ (10 lowast 8) = $461

(11)

Expected profit margins (price-cost) arethe profit of 1199091 product = $549 minus $509 = $40unitthe profit of 1199092 product = $491 minus $461 = $30unit

Maximizing total value of function 11989111198911 = 401199091 + 301199092 (12)

Maximizing total quality index 11989121198912 = 81199091 + 101199092 (13)

Maximizing levels of two products calculated by De Novoprogramming

max 1198911 = 401199091 + 301199092

max 1198912 = 81199091 + 101199092

st 61199091 + 21199092 le 32

31199091 + 51199092 le 30

141199091 + 61199092 le 55

21199091 + 51199092 le 10

61199091 + 81199092 le 28

1199091 1199092 ge 0

(14)

Maximum 1198911 in profit

max 1198911 997888rarr 1199091 = 371

1199092 = 052

119891lowast

1= 40 times 371 + 30 times 052 = $164

(15)

Maximum 1198912 in total quality index

max 1198912 997888rarr 1199091 = 371

1199092 = 052

119891lowast

2= 8 times 371 + 10 times 052 = $3488

(16)

Minimizing the total cost by considering the followingconstraints

min 5091199091 + 4611199092

st 401199091 + 301199092 le 164

81199091 + 101199092 le 3488

(17)

Maximum 1198911 in profit

max 1198911 997888rarr 1199091 = 371

1199092 = 052

119891lowast

1= 40 times 371 + 30 times 052 = $164

(18)

Maximum 1198912 in total quality index

max 1198912 997888rarr 1199091 = 371

1199092 = 052

119891lowast

2= 8 times 371 + 10 times 052 = $3488

(19)

Cost of the newly designed system

(28 times 2328) + (35 times 1368) + (10 times 5506)

+ (18 times 1002) + (10 times 2642) = $21258(20)

The new portfolio of resources proposed by the consultant isshown in Table 2

The result of data analysis shows that the new planningsystem cost is $21258

The given resource portfolio cost is $2956 Comparisonbetween original and new planning model we found outthat the new planning model can reduce cost to $8305 andthat new resource planning model is one reliable modelConsequently the IC design service firm can follow the newplanningmodel ofDe novo programming to process resourceintegration

462 The Case of IC Design Service Firm The IC designservice firm is a professional workshop that focuses on theIC design task dependent on what design they can provideservice The IC design service task is also a project-basedplanning under a large RampDproject with business developingstrategy A project-based firm uses external delivery projectsfor business purposes [47 48] This study utilizes a real caseof IC design project management to practice on IC designservice firm Project planning may include many subprojectsthat depend on different cases of customerrsquos demand Centralfeatures of project management were identified according toindividual project uniqueness human resources of projectand business network complexity discontinuity of demandand relationships between other projects and considerablefinancial commitment of the parties [49 50] Conductingaspiration project planning under resource constraint is atarget issue for the IC design service firm This real caseincludes five different resources which included humanpower resource design equipments design materials special

Mathematical Problems in Engineering 9

Table 1 The IC of GPS and cell phone production requiring material

Unit price ($) Material of production processTechnological coefficients

Resource portfolio(units)(Resource requirement)

1199091

1199092

28 Silver wire 6 2 3235 Golden wire 3 5 3010 Silicon Crystal 14 6 5518 Chemistry material 2 5 1010 Conductor material 6 8 28

Table 2 The new resource portfolio planning of IC of GPS and cell phone

Unit price ($) Material of production processTechnological coefficients

Resource portfolio(units)(Resource requirement)

1199091

1199092

28 Silver wire 6 2 232835 Golden wire 3 5 136810 Silicon Crystal 14 6 550618 Chemistry material 2 5 100210 Conductor material 6 8 2642

technical human and design outsourcing with two differentproject teams for the A company customer to performthe IC design task The current study names the twoprojects as the 1198751 and 1198752 project team programs This realplanning program case utilizes the De Novo programmingfor adjustable resource allocation and seeks out minimumcost with maximum performance This work transitions thefive kinds of resources (design equipments design humanpower design material special technical human and design-outsourcing) in the case study to dollar amounts for easy costcalculation Design equipments cost is $380 per unit designhuman power labor cost is $240 per unit design materialresource cost is $200 special technical human cost is $260per unit and design outsourcing cost is $120 per unit

Costs of given resources portfolio

(380 times 26) + (240 times 50) + (200 times 30) + (120 times 10)

+ (260 times 16) = $33240(21)

Unit costs for running each project of the two projects

1198751 = (380 lowast 8) + (240 lowast 10) + (200 lowast 4)

+ (260 lowast 6) = $7800

1198752 = (380 lowast 6) + (240 lowast 4) + (200 lowast 4)

+ (120 lowast 4) = $4520

(22)

Expected profit margins (price cost) are

1198751 = $8200 minus $7800 = $400unit

1198752 = $4820 minus $4520 = $300unit(23)

Maximizing total value of function 1198911

1198911 = 4001199011 + 3001199012 (24)

Maximizing total quality index 1198912

1198912 = 81199011 + 101199012 (25)

Maximizing levels of the two projects calculated by De Novoprogramming

max 1198911 = 4001199011 + 3001199012

max 1198912 = 81199011 + 101199012

st 81199011 + 61199012 le 26

101199011 + 41199012 le 50

41199011 + 41199012 le 30

41199012 le 10

61199011 le 16

1199011 1199012 ge 0

(26)

Maximum 1198911 in profit

max 1198911 997888rarr 1199011 = 267

1199012 = 078

119891lowast

1= 400 times 267 + 300 times 078 = $1300

(27)

10 Mathematical Problems in Engineering

Table 3 1198751and 119875

2project team program require resource of IC design service

Unit price ($) Resources of IC designProject coefficients

Resource portfolio(units)

(Resource requirement)1198751

1198752

380 Design equipments 8 6 26240 Human power resource 10 4 50200 Design material 4 4 30120 Design outsource 0 4 10260 Special technical human 6 0 16

Table 4 The new resource portfolio planning model of 1198751and 119875

2project team program

Unit price ($) Resources of IC designProject coefficients

Resource portfolio(units)(Resource requirement)

1198751

1198752

380 Design equipments 8 6 26240 Human power resource 10 4 325200 Design material 4 4 13120 Design outsource 0 4 0260 Special technical human 6 0 195

Maximum 1198912 in total quality index

max 1198912 997888rarr 1199011 = 267

1199012 = 078

119891lowast

2= 8 times 267 + 10 times 078 = $2916

(28)

Minimizing the total cost by considering the followingconstraints

min 78001199011 + 45201199012

st 1198911 = 4001199011 + 3001199012 le 1300

1198912 = 81199011 + 101199012 le 2916

(29)

Maximum 1198911 in profit

max 1198911 997888rarr 1199011 = 325

1199012 = 0

119891lowast

1= 400 times 325 = $1300

(30)

Maximum 1198912 in total quality index

max 1198912 997888rarr 1199011 = 325

1199012 = 0

119891lowast

2= 8 times 325 = $26

(31)

Cost of new design planning

(380 times 26) + (240 times 325) + (200 times 13) + (260 times 195)

= $25350(32)

New portfolio of resources proposed by the consultant isshown in Table 4

47 Discussions

471 The General Case of IC Production Firm The datashow the empirical case study using the De Novo program-ming which remodifies the planning model and adjusts theresources allocation portfolio The new plan model offersthe best optimized resource integration and reduces losingand enhances firmrsquos competitiveness Technical problemsprogramming are discussed as follows

(i) The original plan needs a total cost of $2956 withgiven resources portfolios

[(28 times 32) + (35 times 30) + (10 times 55) + (18 times 10)

+ (10 times 28) = $2956] (33)

(ii) The new planning model of resource allocation onlyrequires cost of $21258

[(28 times 2328) + (35 times 1368) + (10 times 5506)

+ (18 times 1002) + (10 times 2642) = $21258] (34)

(iii) The newly planning model can save $8302 costConsequently firm seeks new planning model tointegrate resource

$2956 minus $21258 = $8302 (35)

(iv) The newly planning model use the De Novo pro-gramming to reduce resource loss and get betterperformance than the original planning model Thispaper also utilizes the empirical case examination ofDe Novo programming for IC design service firmperformance The new planning model obtains muchbetter resource integration

Mathematical Problems in Engineering 11

472 The Case of IC Design Service Firm Firms have massproduction capacity limitation per one time period due tolimited resources especially for the preceding competitivefacility location problem on the decentralized supply chain[51] The IC design service firm is a standard project-basedprogrammodel Many project teams program through coop-eration under a large design program for different customerare needed This study utilizes a case study of IC design firmfor planning an IC design to customer serviceThis case studyincludes two project teams The 1198751 and 1198752 project with twodifferent plan programming on IC design see (Table 3) Oneproject team leader prefers hiring a special technical engineerfor the IC design task that can maintain the IC design keytechnician and train other staffThe other project team leaderprefers outsourcing design that reduces cost The IC designservice firm chooses the alternative optimal project teambased on the result of De Novo programming

The resource portfolio of firmrsquos original cost is as follows

(380 times 26) + (240 times 50) + (200 times 30) + (120 times 10)

+ (260 times 16) = $33240(36)

The cost of newly planning model through the De Novoprogramming is as follows

(380 times 26) + (240 times 325) + (200 times 13) + (260 times 195)

= $25350(37)

The new planning model saves cost as follows

$33240 minus $25350 = $7890 (38)

The IC design firm adopting1198751 project team cost is as follows

1199011 (8 times 26) + (10 times 325) + (4 times 13) + (6 times 195) = $702(39)

The IC design firm adopting1198752 project team cost is as follows

1199012 (6 times 26) + (4 times 325) + (4 times 13) = $338 (40)

The result of data analysis shows through the De Novoprogramming method the IC design service firm that canfind out which project team is the optimal solution In thisempirical case adopting 1198752 project team can save design costmuchmore than1198751 projectThe new planningmodel reduces$7890 in cost compared with the original planning modelSo the newly planning model is the best choice for achievingaspiration goal

5 Conclusion

The IC semiconductor industry faces uncertainty anddynamic external environment especially at IC design servicefirm that needs one resource integrating programmingmodelfor new product development [52] Adopting an efficientstrategic alliancesresource integrating planning model is akey issue for achieving firmrsquos business goals especially astheir resource was constrainedThe present study considers a

planningmodel based on resource constraintsThis study uti-lizes the De Novo programming for achieving firmrsquos strategicgoal The De Novo programming can help firms find out anoptimal resource integration base on firm financial considerThis planning model provided reliable systematize approachthat combines alliances from external resources and changethem to internal resources application The project team1198752 adopting open innovation concept through outsourcingreduces design cost and enhances design performance DeNovo programming not only obtains optimal resource inte-gration but also promotes the conceptual of strategic alliancesfor firmrsquos cooperation between each other

51 Management Implication This research aims to create aresource integrating plan model through IC design servicefirm as an empirical case for examining the result of theplanning model The planning model is based on openinnovation theory exploring the IC design service firmwhichintegrates IC design resources and makes optimal resourcesintegrate in IC design service firm The planning model ofDe Novo programming is not only for IC design service firmbut also can apply to the other industrial implementationstrategic allianceresource integration This planning modelis a universal model for the other industry fields

52 Suggestion The IC design service industry is a veryspecial industry We suggest the extend to the other industrythat can find out different results Suggestion utilizes the otherapproach of MCDMmethod exploring different case studiesof industry

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

The authors appreciate two blind reviewers who have pro-vided very excellent comments These comments are verygood for the authors to improve the quality of the paperAlso thanks to the editor Professor Chang for offering lots ofexcellent suggestions that helped enhance this paperrsquos quality

References

[1] R Varadarajan ldquoOutsourcing think more expansivelyrdquo Journalof Business Research vol 62 no 11 pp 1165ndash1172 2009

[2] G Forgionne and Z Guo ldquoInternal supply chain coordinationin the electric utility industryrdquo European Journal of OperationalResearch vol 196 no 2 pp 619ndash627 2009

[3] R George and R Kabir ldquoBusiness groups and profit redistribu-tion a boon or bane for firmsrdquo Journal of Business Research vol61 no 9 pp 1004ndash1014 2008

[4] L Duckstein and S Opricovic ldquoMultiobjective optimization inriver basin developmentrdquoWater Resources Research vol 16 no1 pp 14ndash20 1980

12 Mathematical Problems in Engineering

[5] M Zeleny ldquoA case study in multiple objective design DeNovo programmingrdquo inMultiple Criteria Analysis OperationalMethods vol 1 pp 37ndash52 1981

[6] M Zeleny ldquoOptimal system design with multiple criteria DeNovo programming approachrdquo Engineering Costs and Produc-tion Economics vol 10 no 1 pp 89ndash94 1986

[7] S Christophersona M Kitsonb and J Michiec ldquoInnovationnetworks and knowledge exchangerdquo Cambridge Journal ofRegions Economy and Society vol 1 no 2 pp 165ndash173 2008

[8] H W Chesbrough Open Innovation The New Imperativefor Creating and Profiting from Technology Harvard BusinessSchool Press Boston Mass USA 2003

[9] J B Barney ldquoFirm resources and sustained competitive advan-tagerdquo Advances in Strategic Management vol 17 pp 203ndash2272000

[10] B Wernerfelt ldquoA resource-based view of the firmrdquo StrategicManagement Journal vol 5 no 2 pp 171ndash180 1984

[11] T R Holcomb and M A Hitt ldquoToward a model of strategicoutsourcingrdquo Journal of Operations Management vol 25 no 2pp 464ndash481 2007

[12] D J Collis ldquoA resource-based analysis of global competitionthe case of the bearings industryrdquo Strategic Management Jour-nal vol 12 no 1 pp 49ndash68 1991

[13] E Penrose The Theory of the Growth of the Firm John Wileyand Sons New York NY USA 1959

[14] E Anderson andH Gatignon ldquoModes of foreign entry a trans-action cost analysis and propositionsrdquo Journal of InternationalBusiness Studies vol 17 no 3 pp 1ndash26 1986

[15] J Chimhanzi and R E Morgan ldquoExplanations from themarketinghuman resources dyad formarketing strategy imple-mentation effectiveness in service firmsrdquo Journal of BusinessResearch vol 58 no 6 pp 787ndash796 2005

[16] J Fahy ldquoA resource-based analysis of sustainable competitiveadvantage in a global environmentrdquo International BusinessReview vol 11 no 1 pp 57ndash78 2002

[17] M A Harrison R E Hoskisson and D Ireland ldquoSynergies andpost acquisition performance differences versus similarities inresource allocationsrdquo Journal of Management vol 17 no 1 pp173ndash190 1991

[18] D J Teece G Pisano and A Shuen ldquoDynamic capabilities andstrategic managementrdquo Strategic Management Journal vol 18no 7 pp 509ndash533 1997

[19] V A Varma J F Pekny G E Blau and G V Reklaitis ldquoAframework for addressing stochastic and combinatorial aspectsof scheduling and resource allocation in pharmaceutical RampDpipelinesrdquoComputers andChemical Engineering vol 32 no 4-5pp 1000ndash1015 2008

[20] R Kolisch ldquoEfficient priority rules for the resource-constrainedproject scheduling problemrdquo Journal of Operations Manage-ment vol 14 no 3 pp 179ndash192 1996

[21] R Kolisch and S Hartmann ldquoExperimental investigationof heuristics for resource-constrained project scheduling anupdaterdquo European Journal of Operational Research vol 174 no1 pp 23ndash37 2006

[22] M J Leiblein ldquoThe choice of organizational governance formand performance predictions from transaction cost resource-based and real options theoriesrdquo Journal of Management vol29 no 6 pp 937ndash961 2003

[23] G CMoore and I Benbasat ldquoDevelopment of an instrument tomeasure the perceptions of adopting an information technologyinnovationrdquo Information Systems Research vol 2 no 3 pp 192ndash222 1991

[24] R E Hoskisson M A Hitt W P Wan and D Yiu ldquoTheoryand research in strategic management swings of a pendulumrdquoJournal of Management vol 25 no 3 pp 417ndash456 1999

[25] C C Miller and L B Cardinal ldquoStrategic planning and firmperformance a synthesis of more than two decades of researchrdquoAcademy of Management Journal vol 37 no 6 pp 1649ndash16651994

[26] S D Hunt A General Theory of Competition Sage ThousandOaks Calif USA 2000

[27] S Hart and C Banbury ldquoHow strategy-making processes canmake a differencerdquo Strategic Management Journal vol 15 no 4pp 251ndash269 1994

[28] E Drakopoulos ldquoEnterprise network planning and designmethodology and applicationrdquo Computer Communications vol22 no 4 pp 340ndash352 1999

[29] V Kumar B Maheshwari and U Kumar ldquoERP systems imple-mentation best practices in Canadian government organiza-tionsrdquoGovernment InformationQuarterly vol 19 no 2 pp 147ndash172 2002

[30] W R Lewis ldquoStrategic planningrdquo Hospital Materiel Manage-ment Quarterly vol 10 no 4 pp 57ndash63 1989

[31] D Wainwright and T Waring ldquoThree domains for imple-menting integrated information systems redressing the bal-ance between technology strategic and organisational analysisrdquoInternational Journal of InformationManagement vol 24 no 4pp 329ndash346 2004

[32] M Zeleny ldquoOptimal system design with multiple criteria DeNovo programming approachrdquo Engineering Costs and Produc-tion Economics vol 10 no 1 pp 89ndash95 1986

[33] M Zeleny ldquoOptimal given system versus designing optimalsystem the De Novo programming approachrdquo InternationalJournal of General System vol 17 no 3 pp 295ndash307 1990

[34] Y M Zhang G H Huang and X D Zhang ldquoInexact de Novoprogramming for water resources systems planningrdquo EuropeanJournal ofOperational Research vol 199 no 2 pp 531ndash541 2009

[35] M Zeleny ldquoTrade-offs free management via De Novo pro-grammingrdquo International Journal Operations and QuantitativeManagement vol 1 no 1 pp 3ndash13 1995

[36] H Chesbrough W Anhaverbeke and J West Eds Openinnovation Researching a New Paradigm Oxford UniversityPress 2006

[37] J West and S Gallagher ldquoChallenges of open innovation theparadox of firm investment in open-source softwarerdquo R amp DManagement vol 36 no 3 pp 319ndash331 2006

[38] E H Kessler P E Bierly and S Gopalakrishnan ldquoInternalversus external learning in new product development effects onspeed costs and competitive advantagerdquo R amp D Managementvol 30 no 3 pp 213ndash223 2000

[39] O Gassmann and E Enkel ldquoTowards a theory of open innova-tion three core process archetypesrdquo in Proceedings of the RampDManagement Conference (RADMA rsquo04) Lisbon Portugal 2004

[40] L V Kantorovich and T C Koopmans Problems of Applicationof Optimization Methods in Industry Federation of SwedishIndustry 1976

[41] T K Das and B-S Teng ldquoA resource-based theory of strategicalliancesrdquo Journal of Management vol 26 no 1 pp 31ndash61 2000

[42] Z Babic and I Pavic ldquoMulticriterial production planning byDe Novo programming approachrdquo International Journal ofProduction Economics vol 43 no 1 pp 59ndash66 1996

[43] J K C Chen and G H Tzeng ldquoPerspective strategic allianceand resource allocation in supply chain systems through the De

Mathematical Problems in Engineering 13

Novo programming approachrdquo International Journal Sustain-able Strategic Management vol 1 no 3 pp 320ndash339 2009

[44] J-J Huang G-H Tzeng and C-S Ong ldquoMotivation andresource-allocation for strategic alliances through the De NovoperspectiverdquoMathematical and ComputerModelling vol 41 no6-7 pp 711ndash721 2005

[45] J-J Huang G-H Tzeng and C-S Ong ldquoChoosing bestalliance partners and allocating optimal alliance resources usingthe fuzzymulti-objective dummyprogrammingmodelrdquo Journalof the Operational Research Society vol 57 no 10 pp 1216ndash12232006

[46] R M Grant ldquoThe resource-based theory competitive advan-tage implications for strategy formulationrdquo California Manage-ment Review vol 33 no 3 pp 114ndash135 1991

[47] K A Artto and K Wikstrom ldquoWhat is project businessrdquoInternational Journal of Project Management vol 23 no 5 pp343ndash353 2005

[48] J Soderlund ldquoOn the broadening scope of the research onprojects a review and a model for analysisrdquo InternationalJournal of ProjectManagement vol 22 no 8 pp 655ndash667 2004

[49] B Cova P Ghauri and R Salle Project Marketing BeyondCompetitive Bidding JohnWiley amp Sons Chichester UK 2002

[50] T Mandjak and Z Veres ldquoThe D-U-C model and the stages ofthe project marketing processrdquo in Proceedings of the 14th IMPAnnual Conference Proceedings H K Nummela Ed pp 471ndash490 1998

[51] Q Meng Y Huang and R L Cheu ldquoCompetitive facilitylocation on decentralized supply chainsrdquo European Journal ofOperational Research vol 196 no 2 pp 487ndash499 2009

[52] H-M Hsu and W-P Wang ldquoDynamic programming fordelayed product differentiationrdquo European Journal of Opera-tional Research vol 156 no 1 pp 183ndash193 2004

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Mathematical Problems in Engineering

Hindawi Publishing Corporationhttpwwwhindawicom

Differential EquationsInternational Journal of

Volume 2014

Applied MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Mathematical PhysicsAdvances in

Complex AnalysisJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

OptimizationJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Operations ResearchAdvances in

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Function Spaces

Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of Mathematics and Mathematical Sciences

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Algebra

Discrete Dynamics in Nature and Society

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Decision SciencesAdvances in

Discrete MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom

Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Stochastic AnalysisInternational Journal of

Mathematical Problems in Engineering 9

Table 1 The IC of GPS and cell phone production requiring material

Unit price ($) Material of production processTechnological coefficients

Resource portfolio(units)(Resource requirement)

1199091

1199092

28 Silver wire 6 2 3235 Golden wire 3 5 3010 Silicon Crystal 14 6 5518 Chemistry material 2 5 1010 Conductor material 6 8 28

Table 2 The new resource portfolio planning of IC of GPS and cell phone

Unit price ($) Material of production processTechnological coefficients

Resource portfolio(units)(Resource requirement)

1199091

1199092

28 Silver wire 6 2 232835 Golden wire 3 5 136810 Silicon Crystal 14 6 550618 Chemistry material 2 5 100210 Conductor material 6 8 2642

technical human and design outsourcing with two differentproject teams for the A company customer to performthe IC design task The current study names the twoprojects as the 1198751 and 1198752 project team programs This realplanning program case utilizes the De Novo programmingfor adjustable resource allocation and seeks out minimumcost with maximum performance This work transitions thefive kinds of resources (design equipments design humanpower design material special technical human and design-outsourcing) in the case study to dollar amounts for easy costcalculation Design equipments cost is $380 per unit designhuman power labor cost is $240 per unit design materialresource cost is $200 special technical human cost is $260per unit and design outsourcing cost is $120 per unit

Costs of given resources portfolio

(380 times 26) + (240 times 50) + (200 times 30) + (120 times 10)

+ (260 times 16) = $33240(21)

Unit costs for running each project of the two projects

1198751 = (380 lowast 8) + (240 lowast 10) + (200 lowast 4)

+ (260 lowast 6) = $7800

1198752 = (380 lowast 6) + (240 lowast 4) + (200 lowast 4)

+ (120 lowast 4) = $4520

(22)

Expected profit margins (price cost) are

1198751 = $8200 minus $7800 = $400unit

1198752 = $4820 minus $4520 = $300unit(23)

Maximizing total value of function 1198911

1198911 = 4001199011 + 3001199012 (24)

Maximizing total quality index 1198912

1198912 = 81199011 + 101199012 (25)

Maximizing levels of the two projects calculated by De Novoprogramming

max 1198911 = 4001199011 + 3001199012

max 1198912 = 81199011 + 101199012

st 81199011 + 61199012 le 26

101199011 + 41199012 le 50

41199011 + 41199012 le 30

41199012 le 10

61199011 le 16

1199011 1199012 ge 0

(26)

Maximum 1198911 in profit

max 1198911 997888rarr 1199011 = 267

1199012 = 078

119891lowast

1= 400 times 267 + 300 times 078 = $1300

(27)

10 Mathematical Problems in Engineering

Table 3 1198751and 119875

2project team program require resource of IC design service

Unit price ($) Resources of IC designProject coefficients

Resource portfolio(units)

(Resource requirement)1198751

1198752

380 Design equipments 8 6 26240 Human power resource 10 4 50200 Design material 4 4 30120 Design outsource 0 4 10260 Special technical human 6 0 16

Table 4 The new resource portfolio planning model of 1198751and 119875

2project team program

Unit price ($) Resources of IC designProject coefficients

Resource portfolio(units)(Resource requirement)

1198751

1198752

380 Design equipments 8 6 26240 Human power resource 10 4 325200 Design material 4 4 13120 Design outsource 0 4 0260 Special technical human 6 0 195

Maximum 1198912 in total quality index

max 1198912 997888rarr 1199011 = 267

1199012 = 078

119891lowast

2= 8 times 267 + 10 times 078 = $2916

(28)

Minimizing the total cost by considering the followingconstraints

min 78001199011 + 45201199012

st 1198911 = 4001199011 + 3001199012 le 1300

1198912 = 81199011 + 101199012 le 2916

(29)

Maximum 1198911 in profit

max 1198911 997888rarr 1199011 = 325

1199012 = 0

119891lowast

1= 400 times 325 = $1300

(30)

Maximum 1198912 in total quality index

max 1198912 997888rarr 1199011 = 325

1199012 = 0

119891lowast

2= 8 times 325 = $26

(31)

Cost of new design planning

(380 times 26) + (240 times 325) + (200 times 13) + (260 times 195)

= $25350(32)

New portfolio of resources proposed by the consultant isshown in Table 4

47 Discussions

471 The General Case of IC Production Firm The datashow the empirical case study using the De Novo program-ming which remodifies the planning model and adjusts theresources allocation portfolio The new plan model offersthe best optimized resource integration and reduces losingand enhances firmrsquos competitiveness Technical problemsprogramming are discussed as follows

(i) The original plan needs a total cost of $2956 withgiven resources portfolios

[(28 times 32) + (35 times 30) + (10 times 55) + (18 times 10)

+ (10 times 28) = $2956] (33)

(ii) The new planning model of resource allocation onlyrequires cost of $21258

[(28 times 2328) + (35 times 1368) + (10 times 5506)

+ (18 times 1002) + (10 times 2642) = $21258] (34)

(iii) The newly planning model can save $8302 costConsequently firm seeks new planning model tointegrate resource

$2956 minus $21258 = $8302 (35)

(iv) The newly planning model use the De Novo pro-gramming to reduce resource loss and get betterperformance than the original planning model Thispaper also utilizes the empirical case examination ofDe Novo programming for IC design service firmperformance The new planning model obtains muchbetter resource integration

Mathematical Problems in Engineering 11

472 The Case of IC Design Service Firm Firms have massproduction capacity limitation per one time period due tolimited resources especially for the preceding competitivefacility location problem on the decentralized supply chain[51] The IC design service firm is a standard project-basedprogrammodel Many project teams program through coop-eration under a large design program for different customerare needed This study utilizes a case study of IC design firmfor planning an IC design to customer serviceThis case studyincludes two project teams The 1198751 and 1198752 project with twodifferent plan programming on IC design see (Table 3) Oneproject team leader prefers hiring a special technical engineerfor the IC design task that can maintain the IC design keytechnician and train other staffThe other project team leaderprefers outsourcing design that reduces cost The IC designservice firm chooses the alternative optimal project teambased on the result of De Novo programming

The resource portfolio of firmrsquos original cost is as follows

(380 times 26) + (240 times 50) + (200 times 30) + (120 times 10)

+ (260 times 16) = $33240(36)

The cost of newly planning model through the De Novoprogramming is as follows

(380 times 26) + (240 times 325) + (200 times 13) + (260 times 195)

= $25350(37)

The new planning model saves cost as follows

$33240 minus $25350 = $7890 (38)

The IC design firm adopting1198751 project team cost is as follows

1199011 (8 times 26) + (10 times 325) + (4 times 13) + (6 times 195) = $702(39)

The IC design firm adopting1198752 project team cost is as follows

1199012 (6 times 26) + (4 times 325) + (4 times 13) = $338 (40)

The result of data analysis shows through the De Novoprogramming method the IC design service firm that canfind out which project team is the optimal solution In thisempirical case adopting 1198752 project team can save design costmuchmore than1198751 projectThe new planningmodel reduces$7890 in cost compared with the original planning modelSo the newly planning model is the best choice for achievingaspiration goal

5 Conclusion

The IC semiconductor industry faces uncertainty anddynamic external environment especially at IC design servicefirm that needs one resource integrating programmingmodelfor new product development [52] Adopting an efficientstrategic alliancesresource integrating planning model is akey issue for achieving firmrsquos business goals especially astheir resource was constrainedThe present study considers a

planningmodel based on resource constraintsThis study uti-lizes the De Novo programming for achieving firmrsquos strategicgoal The De Novo programming can help firms find out anoptimal resource integration base on firm financial considerThis planning model provided reliable systematize approachthat combines alliances from external resources and changethem to internal resources application The project team1198752 adopting open innovation concept through outsourcingreduces design cost and enhances design performance DeNovo programming not only obtains optimal resource inte-gration but also promotes the conceptual of strategic alliancesfor firmrsquos cooperation between each other

51 Management Implication This research aims to create aresource integrating plan model through IC design servicefirm as an empirical case for examining the result of theplanning model The planning model is based on openinnovation theory exploring the IC design service firmwhichintegrates IC design resources and makes optimal resourcesintegrate in IC design service firm The planning model ofDe Novo programming is not only for IC design service firmbut also can apply to the other industrial implementationstrategic allianceresource integration This planning modelis a universal model for the other industry fields

52 Suggestion The IC design service industry is a veryspecial industry We suggest the extend to the other industrythat can find out different results Suggestion utilizes the otherapproach of MCDMmethod exploring different case studiesof industry

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

The authors appreciate two blind reviewers who have pro-vided very excellent comments These comments are verygood for the authors to improve the quality of the paperAlso thanks to the editor Professor Chang for offering lots ofexcellent suggestions that helped enhance this paperrsquos quality

References

[1] R Varadarajan ldquoOutsourcing think more expansivelyrdquo Journalof Business Research vol 62 no 11 pp 1165ndash1172 2009

[2] G Forgionne and Z Guo ldquoInternal supply chain coordinationin the electric utility industryrdquo European Journal of OperationalResearch vol 196 no 2 pp 619ndash627 2009

[3] R George and R Kabir ldquoBusiness groups and profit redistribu-tion a boon or bane for firmsrdquo Journal of Business Research vol61 no 9 pp 1004ndash1014 2008

[4] L Duckstein and S Opricovic ldquoMultiobjective optimization inriver basin developmentrdquoWater Resources Research vol 16 no1 pp 14ndash20 1980

12 Mathematical Problems in Engineering

[5] M Zeleny ldquoA case study in multiple objective design DeNovo programmingrdquo inMultiple Criteria Analysis OperationalMethods vol 1 pp 37ndash52 1981

[6] M Zeleny ldquoOptimal system design with multiple criteria DeNovo programming approachrdquo Engineering Costs and Produc-tion Economics vol 10 no 1 pp 89ndash94 1986

[7] S Christophersona M Kitsonb and J Michiec ldquoInnovationnetworks and knowledge exchangerdquo Cambridge Journal ofRegions Economy and Society vol 1 no 2 pp 165ndash173 2008

[8] H W Chesbrough Open Innovation The New Imperativefor Creating and Profiting from Technology Harvard BusinessSchool Press Boston Mass USA 2003

[9] J B Barney ldquoFirm resources and sustained competitive advan-tagerdquo Advances in Strategic Management vol 17 pp 203ndash2272000

[10] B Wernerfelt ldquoA resource-based view of the firmrdquo StrategicManagement Journal vol 5 no 2 pp 171ndash180 1984

[11] T R Holcomb and M A Hitt ldquoToward a model of strategicoutsourcingrdquo Journal of Operations Management vol 25 no 2pp 464ndash481 2007

[12] D J Collis ldquoA resource-based analysis of global competitionthe case of the bearings industryrdquo Strategic Management Jour-nal vol 12 no 1 pp 49ndash68 1991

[13] E Penrose The Theory of the Growth of the Firm John Wileyand Sons New York NY USA 1959

[14] E Anderson andH Gatignon ldquoModes of foreign entry a trans-action cost analysis and propositionsrdquo Journal of InternationalBusiness Studies vol 17 no 3 pp 1ndash26 1986

[15] J Chimhanzi and R E Morgan ldquoExplanations from themarketinghuman resources dyad formarketing strategy imple-mentation effectiveness in service firmsrdquo Journal of BusinessResearch vol 58 no 6 pp 787ndash796 2005

[16] J Fahy ldquoA resource-based analysis of sustainable competitiveadvantage in a global environmentrdquo International BusinessReview vol 11 no 1 pp 57ndash78 2002

[17] M A Harrison R E Hoskisson and D Ireland ldquoSynergies andpost acquisition performance differences versus similarities inresource allocationsrdquo Journal of Management vol 17 no 1 pp173ndash190 1991

[18] D J Teece G Pisano and A Shuen ldquoDynamic capabilities andstrategic managementrdquo Strategic Management Journal vol 18no 7 pp 509ndash533 1997

[19] V A Varma J F Pekny G E Blau and G V Reklaitis ldquoAframework for addressing stochastic and combinatorial aspectsof scheduling and resource allocation in pharmaceutical RampDpipelinesrdquoComputers andChemical Engineering vol 32 no 4-5pp 1000ndash1015 2008

[20] R Kolisch ldquoEfficient priority rules for the resource-constrainedproject scheduling problemrdquo Journal of Operations Manage-ment vol 14 no 3 pp 179ndash192 1996

[21] R Kolisch and S Hartmann ldquoExperimental investigationof heuristics for resource-constrained project scheduling anupdaterdquo European Journal of Operational Research vol 174 no1 pp 23ndash37 2006

[22] M J Leiblein ldquoThe choice of organizational governance formand performance predictions from transaction cost resource-based and real options theoriesrdquo Journal of Management vol29 no 6 pp 937ndash961 2003

[23] G CMoore and I Benbasat ldquoDevelopment of an instrument tomeasure the perceptions of adopting an information technologyinnovationrdquo Information Systems Research vol 2 no 3 pp 192ndash222 1991

[24] R E Hoskisson M A Hitt W P Wan and D Yiu ldquoTheoryand research in strategic management swings of a pendulumrdquoJournal of Management vol 25 no 3 pp 417ndash456 1999

[25] C C Miller and L B Cardinal ldquoStrategic planning and firmperformance a synthesis of more than two decades of researchrdquoAcademy of Management Journal vol 37 no 6 pp 1649ndash16651994

[26] S D Hunt A General Theory of Competition Sage ThousandOaks Calif USA 2000

[27] S Hart and C Banbury ldquoHow strategy-making processes canmake a differencerdquo Strategic Management Journal vol 15 no 4pp 251ndash269 1994

[28] E Drakopoulos ldquoEnterprise network planning and designmethodology and applicationrdquo Computer Communications vol22 no 4 pp 340ndash352 1999

[29] V Kumar B Maheshwari and U Kumar ldquoERP systems imple-mentation best practices in Canadian government organiza-tionsrdquoGovernment InformationQuarterly vol 19 no 2 pp 147ndash172 2002

[30] W R Lewis ldquoStrategic planningrdquo Hospital Materiel Manage-ment Quarterly vol 10 no 4 pp 57ndash63 1989

[31] D Wainwright and T Waring ldquoThree domains for imple-menting integrated information systems redressing the bal-ance between technology strategic and organisational analysisrdquoInternational Journal of InformationManagement vol 24 no 4pp 329ndash346 2004

[32] M Zeleny ldquoOptimal system design with multiple criteria DeNovo programming approachrdquo Engineering Costs and Produc-tion Economics vol 10 no 1 pp 89ndash95 1986

[33] M Zeleny ldquoOptimal given system versus designing optimalsystem the De Novo programming approachrdquo InternationalJournal of General System vol 17 no 3 pp 295ndash307 1990

[34] Y M Zhang G H Huang and X D Zhang ldquoInexact de Novoprogramming for water resources systems planningrdquo EuropeanJournal ofOperational Research vol 199 no 2 pp 531ndash541 2009

[35] M Zeleny ldquoTrade-offs free management via De Novo pro-grammingrdquo International Journal Operations and QuantitativeManagement vol 1 no 1 pp 3ndash13 1995

[36] H Chesbrough W Anhaverbeke and J West Eds Openinnovation Researching a New Paradigm Oxford UniversityPress 2006

[37] J West and S Gallagher ldquoChallenges of open innovation theparadox of firm investment in open-source softwarerdquo R amp DManagement vol 36 no 3 pp 319ndash331 2006

[38] E H Kessler P E Bierly and S Gopalakrishnan ldquoInternalversus external learning in new product development effects onspeed costs and competitive advantagerdquo R amp D Managementvol 30 no 3 pp 213ndash223 2000

[39] O Gassmann and E Enkel ldquoTowards a theory of open innova-tion three core process archetypesrdquo in Proceedings of the RampDManagement Conference (RADMA rsquo04) Lisbon Portugal 2004

[40] L V Kantorovich and T C Koopmans Problems of Applicationof Optimization Methods in Industry Federation of SwedishIndustry 1976

[41] T K Das and B-S Teng ldquoA resource-based theory of strategicalliancesrdquo Journal of Management vol 26 no 1 pp 31ndash61 2000

[42] Z Babic and I Pavic ldquoMulticriterial production planning byDe Novo programming approachrdquo International Journal ofProduction Economics vol 43 no 1 pp 59ndash66 1996

[43] J K C Chen and G H Tzeng ldquoPerspective strategic allianceand resource allocation in supply chain systems through the De

Mathematical Problems in Engineering 13

Novo programming approachrdquo International Journal Sustain-able Strategic Management vol 1 no 3 pp 320ndash339 2009

[44] J-J Huang G-H Tzeng and C-S Ong ldquoMotivation andresource-allocation for strategic alliances through the De NovoperspectiverdquoMathematical and ComputerModelling vol 41 no6-7 pp 711ndash721 2005

[45] J-J Huang G-H Tzeng and C-S Ong ldquoChoosing bestalliance partners and allocating optimal alliance resources usingthe fuzzymulti-objective dummyprogrammingmodelrdquo Journalof the Operational Research Society vol 57 no 10 pp 1216ndash12232006

[46] R M Grant ldquoThe resource-based theory competitive advan-tage implications for strategy formulationrdquo California Manage-ment Review vol 33 no 3 pp 114ndash135 1991

[47] K A Artto and K Wikstrom ldquoWhat is project businessrdquoInternational Journal of Project Management vol 23 no 5 pp343ndash353 2005

[48] J Soderlund ldquoOn the broadening scope of the research onprojects a review and a model for analysisrdquo InternationalJournal of ProjectManagement vol 22 no 8 pp 655ndash667 2004

[49] B Cova P Ghauri and R Salle Project Marketing BeyondCompetitive Bidding JohnWiley amp Sons Chichester UK 2002

[50] T Mandjak and Z Veres ldquoThe D-U-C model and the stages ofthe project marketing processrdquo in Proceedings of the 14th IMPAnnual Conference Proceedings H K Nummela Ed pp 471ndash490 1998

[51] Q Meng Y Huang and R L Cheu ldquoCompetitive facilitylocation on decentralized supply chainsrdquo European Journal ofOperational Research vol 196 no 2 pp 487ndash499 2009

[52] H-M Hsu and W-P Wang ldquoDynamic programming fordelayed product differentiationrdquo European Journal of Opera-tional Research vol 156 no 1 pp 183ndash193 2004

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Mathematical Problems in Engineering

Hindawi Publishing Corporationhttpwwwhindawicom

Differential EquationsInternational Journal of

Volume 2014

Applied MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Mathematical PhysicsAdvances in

Complex AnalysisJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

OptimizationJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Operations ResearchAdvances in

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Function Spaces

Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of Mathematics and Mathematical Sciences

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Algebra

Discrete Dynamics in Nature and Society

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Decision SciencesAdvances in

Discrete MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom

Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Stochastic AnalysisInternational Journal of

10 Mathematical Problems in Engineering

Table 3 1198751and 119875

2project team program require resource of IC design service

Unit price ($) Resources of IC designProject coefficients

Resource portfolio(units)

(Resource requirement)1198751

1198752

380 Design equipments 8 6 26240 Human power resource 10 4 50200 Design material 4 4 30120 Design outsource 0 4 10260 Special technical human 6 0 16

Table 4 The new resource portfolio planning model of 1198751and 119875

2project team program

Unit price ($) Resources of IC designProject coefficients

Resource portfolio(units)(Resource requirement)

1198751

1198752

380 Design equipments 8 6 26240 Human power resource 10 4 325200 Design material 4 4 13120 Design outsource 0 4 0260 Special technical human 6 0 195

Maximum 1198912 in total quality index

max 1198912 997888rarr 1199011 = 267

1199012 = 078

119891lowast

2= 8 times 267 + 10 times 078 = $2916

(28)

Minimizing the total cost by considering the followingconstraints

min 78001199011 + 45201199012

st 1198911 = 4001199011 + 3001199012 le 1300

1198912 = 81199011 + 101199012 le 2916

(29)

Maximum 1198911 in profit

max 1198911 997888rarr 1199011 = 325

1199012 = 0

119891lowast

1= 400 times 325 = $1300

(30)

Maximum 1198912 in total quality index

max 1198912 997888rarr 1199011 = 325

1199012 = 0

119891lowast

2= 8 times 325 = $26

(31)

Cost of new design planning

(380 times 26) + (240 times 325) + (200 times 13) + (260 times 195)

= $25350(32)

New portfolio of resources proposed by the consultant isshown in Table 4

47 Discussions

471 The General Case of IC Production Firm The datashow the empirical case study using the De Novo program-ming which remodifies the planning model and adjusts theresources allocation portfolio The new plan model offersthe best optimized resource integration and reduces losingand enhances firmrsquos competitiveness Technical problemsprogramming are discussed as follows

(i) The original plan needs a total cost of $2956 withgiven resources portfolios

[(28 times 32) + (35 times 30) + (10 times 55) + (18 times 10)

+ (10 times 28) = $2956] (33)

(ii) The new planning model of resource allocation onlyrequires cost of $21258

[(28 times 2328) + (35 times 1368) + (10 times 5506)

+ (18 times 1002) + (10 times 2642) = $21258] (34)

(iii) The newly planning model can save $8302 costConsequently firm seeks new planning model tointegrate resource

$2956 minus $21258 = $8302 (35)

(iv) The newly planning model use the De Novo pro-gramming to reduce resource loss and get betterperformance than the original planning model Thispaper also utilizes the empirical case examination ofDe Novo programming for IC design service firmperformance The new planning model obtains muchbetter resource integration

Mathematical Problems in Engineering 11

472 The Case of IC Design Service Firm Firms have massproduction capacity limitation per one time period due tolimited resources especially for the preceding competitivefacility location problem on the decentralized supply chain[51] The IC design service firm is a standard project-basedprogrammodel Many project teams program through coop-eration under a large design program for different customerare needed This study utilizes a case study of IC design firmfor planning an IC design to customer serviceThis case studyincludes two project teams The 1198751 and 1198752 project with twodifferent plan programming on IC design see (Table 3) Oneproject team leader prefers hiring a special technical engineerfor the IC design task that can maintain the IC design keytechnician and train other staffThe other project team leaderprefers outsourcing design that reduces cost The IC designservice firm chooses the alternative optimal project teambased on the result of De Novo programming

The resource portfolio of firmrsquos original cost is as follows

(380 times 26) + (240 times 50) + (200 times 30) + (120 times 10)

+ (260 times 16) = $33240(36)

The cost of newly planning model through the De Novoprogramming is as follows

(380 times 26) + (240 times 325) + (200 times 13) + (260 times 195)

= $25350(37)

The new planning model saves cost as follows

$33240 minus $25350 = $7890 (38)

The IC design firm adopting1198751 project team cost is as follows

1199011 (8 times 26) + (10 times 325) + (4 times 13) + (6 times 195) = $702(39)

The IC design firm adopting1198752 project team cost is as follows

1199012 (6 times 26) + (4 times 325) + (4 times 13) = $338 (40)

The result of data analysis shows through the De Novoprogramming method the IC design service firm that canfind out which project team is the optimal solution In thisempirical case adopting 1198752 project team can save design costmuchmore than1198751 projectThe new planningmodel reduces$7890 in cost compared with the original planning modelSo the newly planning model is the best choice for achievingaspiration goal

5 Conclusion

The IC semiconductor industry faces uncertainty anddynamic external environment especially at IC design servicefirm that needs one resource integrating programmingmodelfor new product development [52] Adopting an efficientstrategic alliancesresource integrating planning model is akey issue for achieving firmrsquos business goals especially astheir resource was constrainedThe present study considers a

planningmodel based on resource constraintsThis study uti-lizes the De Novo programming for achieving firmrsquos strategicgoal The De Novo programming can help firms find out anoptimal resource integration base on firm financial considerThis planning model provided reliable systematize approachthat combines alliances from external resources and changethem to internal resources application The project team1198752 adopting open innovation concept through outsourcingreduces design cost and enhances design performance DeNovo programming not only obtains optimal resource inte-gration but also promotes the conceptual of strategic alliancesfor firmrsquos cooperation between each other

51 Management Implication This research aims to create aresource integrating plan model through IC design servicefirm as an empirical case for examining the result of theplanning model The planning model is based on openinnovation theory exploring the IC design service firmwhichintegrates IC design resources and makes optimal resourcesintegrate in IC design service firm The planning model ofDe Novo programming is not only for IC design service firmbut also can apply to the other industrial implementationstrategic allianceresource integration This planning modelis a universal model for the other industry fields

52 Suggestion The IC design service industry is a veryspecial industry We suggest the extend to the other industrythat can find out different results Suggestion utilizes the otherapproach of MCDMmethod exploring different case studiesof industry

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

The authors appreciate two blind reviewers who have pro-vided very excellent comments These comments are verygood for the authors to improve the quality of the paperAlso thanks to the editor Professor Chang for offering lots ofexcellent suggestions that helped enhance this paperrsquos quality

References

[1] R Varadarajan ldquoOutsourcing think more expansivelyrdquo Journalof Business Research vol 62 no 11 pp 1165ndash1172 2009

[2] G Forgionne and Z Guo ldquoInternal supply chain coordinationin the electric utility industryrdquo European Journal of OperationalResearch vol 196 no 2 pp 619ndash627 2009

[3] R George and R Kabir ldquoBusiness groups and profit redistribu-tion a boon or bane for firmsrdquo Journal of Business Research vol61 no 9 pp 1004ndash1014 2008

[4] L Duckstein and S Opricovic ldquoMultiobjective optimization inriver basin developmentrdquoWater Resources Research vol 16 no1 pp 14ndash20 1980

12 Mathematical Problems in Engineering

[5] M Zeleny ldquoA case study in multiple objective design DeNovo programmingrdquo inMultiple Criteria Analysis OperationalMethods vol 1 pp 37ndash52 1981

[6] M Zeleny ldquoOptimal system design with multiple criteria DeNovo programming approachrdquo Engineering Costs and Produc-tion Economics vol 10 no 1 pp 89ndash94 1986

[7] S Christophersona M Kitsonb and J Michiec ldquoInnovationnetworks and knowledge exchangerdquo Cambridge Journal ofRegions Economy and Society vol 1 no 2 pp 165ndash173 2008

[8] H W Chesbrough Open Innovation The New Imperativefor Creating and Profiting from Technology Harvard BusinessSchool Press Boston Mass USA 2003

[9] J B Barney ldquoFirm resources and sustained competitive advan-tagerdquo Advances in Strategic Management vol 17 pp 203ndash2272000

[10] B Wernerfelt ldquoA resource-based view of the firmrdquo StrategicManagement Journal vol 5 no 2 pp 171ndash180 1984

[11] T R Holcomb and M A Hitt ldquoToward a model of strategicoutsourcingrdquo Journal of Operations Management vol 25 no 2pp 464ndash481 2007

[12] D J Collis ldquoA resource-based analysis of global competitionthe case of the bearings industryrdquo Strategic Management Jour-nal vol 12 no 1 pp 49ndash68 1991

[13] E Penrose The Theory of the Growth of the Firm John Wileyand Sons New York NY USA 1959

[14] E Anderson andH Gatignon ldquoModes of foreign entry a trans-action cost analysis and propositionsrdquo Journal of InternationalBusiness Studies vol 17 no 3 pp 1ndash26 1986

[15] J Chimhanzi and R E Morgan ldquoExplanations from themarketinghuman resources dyad formarketing strategy imple-mentation effectiveness in service firmsrdquo Journal of BusinessResearch vol 58 no 6 pp 787ndash796 2005

[16] J Fahy ldquoA resource-based analysis of sustainable competitiveadvantage in a global environmentrdquo International BusinessReview vol 11 no 1 pp 57ndash78 2002

[17] M A Harrison R E Hoskisson and D Ireland ldquoSynergies andpost acquisition performance differences versus similarities inresource allocationsrdquo Journal of Management vol 17 no 1 pp173ndash190 1991

[18] D J Teece G Pisano and A Shuen ldquoDynamic capabilities andstrategic managementrdquo Strategic Management Journal vol 18no 7 pp 509ndash533 1997

[19] V A Varma J F Pekny G E Blau and G V Reklaitis ldquoAframework for addressing stochastic and combinatorial aspectsof scheduling and resource allocation in pharmaceutical RampDpipelinesrdquoComputers andChemical Engineering vol 32 no 4-5pp 1000ndash1015 2008

[20] R Kolisch ldquoEfficient priority rules for the resource-constrainedproject scheduling problemrdquo Journal of Operations Manage-ment vol 14 no 3 pp 179ndash192 1996

[21] R Kolisch and S Hartmann ldquoExperimental investigationof heuristics for resource-constrained project scheduling anupdaterdquo European Journal of Operational Research vol 174 no1 pp 23ndash37 2006

[22] M J Leiblein ldquoThe choice of organizational governance formand performance predictions from transaction cost resource-based and real options theoriesrdquo Journal of Management vol29 no 6 pp 937ndash961 2003

[23] G CMoore and I Benbasat ldquoDevelopment of an instrument tomeasure the perceptions of adopting an information technologyinnovationrdquo Information Systems Research vol 2 no 3 pp 192ndash222 1991

[24] R E Hoskisson M A Hitt W P Wan and D Yiu ldquoTheoryand research in strategic management swings of a pendulumrdquoJournal of Management vol 25 no 3 pp 417ndash456 1999

[25] C C Miller and L B Cardinal ldquoStrategic planning and firmperformance a synthesis of more than two decades of researchrdquoAcademy of Management Journal vol 37 no 6 pp 1649ndash16651994

[26] S D Hunt A General Theory of Competition Sage ThousandOaks Calif USA 2000

[27] S Hart and C Banbury ldquoHow strategy-making processes canmake a differencerdquo Strategic Management Journal vol 15 no 4pp 251ndash269 1994

[28] E Drakopoulos ldquoEnterprise network planning and designmethodology and applicationrdquo Computer Communications vol22 no 4 pp 340ndash352 1999

[29] V Kumar B Maheshwari and U Kumar ldquoERP systems imple-mentation best practices in Canadian government organiza-tionsrdquoGovernment InformationQuarterly vol 19 no 2 pp 147ndash172 2002

[30] W R Lewis ldquoStrategic planningrdquo Hospital Materiel Manage-ment Quarterly vol 10 no 4 pp 57ndash63 1989

[31] D Wainwright and T Waring ldquoThree domains for imple-menting integrated information systems redressing the bal-ance between technology strategic and organisational analysisrdquoInternational Journal of InformationManagement vol 24 no 4pp 329ndash346 2004

[32] M Zeleny ldquoOptimal system design with multiple criteria DeNovo programming approachrdquo Engineering Costs and Produc-tion Economics vol 10 no 1 pp 89ndash95 1986

[33] M Zeleny ldquoOptimal given system versus designing optimalsystem the De Novo programming approachrdquo InternationalJournal of General System vol 17 no 3 pp 295ndash307 1990

[34] Y M Zhang G H Huang and X D Zhang ldquoInexact de Novoprogramming for water resources systems planningrdquo EuropeanJournal ofOperational Research vol 199 no 2 pp 531ndash541 2009

[35] M Zeleny ldquoTrade-offs free management via De Novo pro-grammingrdquo International Journal Operations and QuantitativeManagement vol 1 no 1 pp 3ndash13 1995

[36] H Chesbrough W Anhaverbeke and J West Eds Openinnovation Researching a New Paradigm Oxford UniversityPress 2006

[37] J West and S Gallagher ldquoChallenges of open innovation theparadox of firm investment in open-source softwarerdquo R amp DManagement vol 36 no 3 pp 319ndash331 2006

[38] E H Kessler P E Bierly and S Gopalakrishnan ldquoInternalversus external learning in new product development effects onspeed costs and competitive advantagerdquo R amp D Managementvol 30 no 3 pp 213ndash223 2000

[39] O Gassmann and E Enkel ldquoTowards a theory of open innova-tion three core process archetypesrdquo in Proceedings of the RampDManagement Conference (RADMA rsquo04) Lisbon Portugal 2004

[40] L V Kantorovich and T C Koopmans Problems of Applicationof Optimization Methods in Industry Federation of SwedishIndustry 1976

[41] T K Das and B-S Teng ldquoA resource-based theory of strategicalliancesrdquo Journal of Management vol 26 no 1 pp 31ndash61 2000

[42] Z Babic and I Pavic ldquoMulticriterial production planning byDe Novo programming approachrdquo International Journal ofProduction Economics vol 43 no 1 pp 59ndash66 1996

[43] J K C Chen and G H Tzeng ldquoPerspective strategic allianceand resource allocation in supply chain systems through the De

Mathematical Problems in Engineering 13

Novo programming approachrdquo International Journal Sustain-able Strategic Management vol 1 no 3 pp 320ndash339 2009

[44] J-J Huang G-H Tzeng and C-S Ong ldquoMotivation andresource-allocation for strategic alliances through the De NovoperspectiverdquoMathematical and ComputerModelling vol 41 no6-7 pp 711ndash721 2005

[45] J-J Huang G-H Tzeng and C-S Ong ldquoChoosing bestalliance partners and allocating optimal alliance resources usingthe fuzzymulti-objective dummyprogrammingmodelrdquo Journalof the Operational Research Society vol 57 no 10 pp 1216ndash12232006

[46] R M Grant ldquoThe resource-based theory competitive advan-tage implications for strategy formulationrdquo California Manage-ment Review vol 33 no 3 pp 114ndash135 1991

[47] K A Artto and K Wikstrom ldquoWhat is project businessrdquoInternational Journal of Project Management vol 23 no 5 pp343ndash353 2005

[48] J Soderlund ldquoOn the broadening scope of the research onprojects a review and a model for analysisrdquo InternationalJournal of ProjectManagement vol 22 no 8 pp 655ndash667 2004

[49] B Cova P Ghauri and R Salle Project Marketing BeyondCompetitive Bidding JohnWiley amp Sons Chichester UK 2002

[50] T Mandjak and Z Veres ldquoThe D-U-C model and the stages ofthe project marketing processrdquo in Proceedings of the 14th IMPAnnual Conference Proceedings H K Nummela Ed pp 471ndash490 1998

[51] Q Meng Y Huang and R L Cheu ldquoCompetitive facilitylocation on decentralized supply chainsrdquo European Journal ofOperational Research vol 196 no 2 pp 487ndash499 2009

[52] H-M Hsu and W-P Wang ldquoDynamic programming fordelayed product differentiationrdquo European Journal of Opera-tional Research vol 156 no 1 pp 183ndash193 2004

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Mathematical Problems in Engineering

Hindawi Publishing Corporationhttpwwwhindawicom

Differential EquationsInternational Journal of

Volume 2014

Applied MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Mathematical PhysicsAdvances in

Complex AnalysisJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

OptimizationJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Operations ResearchAdvances in

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Function Spaces

Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of Mathematics and Mathematical Sciences

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Algebra

Discrete Dynamics in Nature and Society

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Decision SciencesAdvances in

Discrete MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom

Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Stochastic AnalysisInternational Journal of

Mathematical Problems in Engineering 11

472 The Case of IC Design Service Firm Firms have massproduction capacity limitation per one time period due tolimited resources especially for the preceding competitivefacility location problem on the decentralized supply chain[51] The IC design service firm is a standard project-basedprogrammodel Many project teams program through coop-eration under a large design program for different customerare needed This study utilizes a case study of IC design firmfor planning an IC design to customer serviceThis case studyincludes two project teams The 1198751 and 1198752 project with twodifferent plan programming on IC design see (Table 3) Oneproject team leader prefers hiring a special technical engineerfor the IC design task that can maintain the IC design keytechnician and train other staffThe other project team leaderprefers outsourcing design that reduces cost The IC designservice firm chooses the alternative optimal project teambased on the result of De Novo programming

The resource portfolio of firmrsquos original cost is as follows

(380 times 26) + (240 times 50) + (200 times 30) + (120 times 10)

+ (260 times 16) = $33240(36)

The cost of newly planning model through the De Novoprogramming is as follows

(380 times 26) + (240 times 325) + (200 times 13) + (260 times 195)

= $25350(37)

The new planning model saves cost as follows

$33240 minus $25350 = $7890 (38)

The IC design firm adopting1198751 project team cost is as follows

1199011 (8 times 26) + (10 times 325) + (4 times 13) + (6 times 195) = $702(39)

The IC design firm adopting1198752 project team cost is as follows

1199012 (6 times 26) + (4 times 325) + (4 times 13) = $338 (40)

The result of data analysis shows through the De Novoprogramming method the IC design service firm that canfind out which project team is the optimal solution In thisempirical case adopting 1198752 project team can save design costmuchmore than1198751 projectThe new planningmodel reduces$7890 in cost compared with the original planning modelSo the newly planning model is the best choice for achievingaspiration goal

5 Conclusion

The IC semiconductor industry faces uncertainty anddynamic external environment especially at IC design servicefirm that needs one resource integrating programmingmodelfor new product development [52] Adopting an efficientstrategic alliancesresource integrating planning model is akey issue for achieving firmrsquos business goals especially astheir resource was constrainedThe present study considers a

planningmodel based on resource constraintsThis study uti-lizes the De Novo programming for achieving firmrsquos strategicgoal The De Novo programming can help firms find out anoptimal resource integration base on firm financial considerThis planning model provided reliable systematize approachthat combines alliances from external resources and changethem to internal resources application The project team1198752 adopting open innovation concept through outsourcingreduces design cost and enhances design performance DeNovo programming not only obtains optimal resource inte-gration but also promotes the conceptual of strategic alliancesfor firmrsquos cooperation between each other

51 Management Implication This research aims to create aresource integrating plan model through IC design servicefirm as an empirical case for examining the result of theplanning model The planning model is based on openinnovation theory exploring the IC design service firmwhichintegrates IC design resources and makes optimal resourcesintegrate in IC design service firm The planning model ofDe Novo programming is not only for IC design service firmbut also can apply to the other industrial implementationstrategic allianceresource integration This planning modelis a universal model for the other industry fields

52 Suggestion The IC design service industry is a veryspecial industry We suggest the extend to the other industrythat can find out different results Suggestion utilizes the otherapproach of MCDMmethod exploring different case studiesof industry

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

The authors appreciate two blind reviewers who have pro-vided very excellent comments These comments are verygood for the authors to improve the quality of the paperAlso thanks to the editor Professor Chang for offering lots ofexcellent suggestions that helped enhance this paperrsquos quality

References

[1] R Varadarajan ldquoOutsourcing think more expansivelyrdquo Journalof Business Research vol 62 no 11 pp 1165ndash1172 2009

[2] G Forgionne and Z Guo ldquoInternal supply chain coordinationin the electric utility industryrdquo European Journal of OperationalResearch vol 196 no 2 pp 619ndash627 2009

[3] R George and R Kabir ldquoBusiness groups and profit redistribu-tion a boon or bane for firmsrdquo Journal of Business Research vol61 no 9 pp 1004ndash1014 2008

[4] L Duckstein and S Opricovic ldquoMultiobjective optimization inriver basin developmentrdquoWater Resources Research vol 16 no1 pp 14ndash20 1980

12 Mathematical Problems in Engineering

[5] M Zeleny ldquoA case study in multiple objective design DeNovo programmingrdquo inMultiple Criteria Analysis OperationalMethods vol 1 pp 37ndash52 1981

[6] M Zeleny ldquoOptimal system design with multiple criteria DeNovo programming approachrdquo Engineering Costs and Produc-tion Economics vol 10 no 1 pp 89ndash94 1986

[7] S Christophersona M Kitsonb and J Michiec ldquoInnovationnetworks and knowledge exchangerdquo Cambridge Journal ofRegions Economy and Society vol 1 no 2 pp 165ndash173 2008

[8] H W Chesbrough Open Innovation The New Imperativefor Creating and Profiting from Technology Harvard BusinessSchool Press Boston Mass USA 2003

[9] J B Barney ldquoFirm resources and sustained competitive advan-tagerdquo Advances in Strategic Management vol 17 pp 203ndash2272000

[10] B Wernerfelt ldquoA resource-based view of the firmrdquo StrategicManagement Journal vol 5 no 2 pp 171ndash180 1984

[11] T R Holcomb and M A Hitt ldquoToward a model of strategicoutsourcingrdquo Journal of Operations Management vol 25 no 2pp 464ndash481 2007

[12] D J Collis ldquoA resource-based analysis of global competitionthe case of the bearings industryrdquo Strategic Management Jour-nal vol 12 no 1 pp 49ndash68 1991

[13] E Penrose The Theory of the Growth of the Firm John Wileyand Sons New York NY USA 1959

[14] E Anderson andH Gatignon ldquoModes of foreign entry a trans-action cost analysis and propositionsrdquo Journal of InternationalBusiness Studies vol 17 no 3 pp 1ndash26 1986

[15] J Chimhanzi and R E Morgan ldquoExplanations from themarketinghuman resources dyad formarketing strategy imple-mentation effectiveness in service firmsrdquo Journal of BusinessResearch vol 58 no 6 pp 787ndash796 2005

[16] J Fahy ldquoA resource-based analysis of sustainable competitiveadvantage in a global environmentrdquo International BusinessReview vol 11 no 1 pp 57ndash78 2002

[17] M A Harrison R E Hoskisson and D Ireland ldquoSynergies andpost acquisition performance differences versus similarities inresource allocationsrdquo Journal of Management vol 17 no 1 pp173ndash190 1991

[18] D J Teece G Pisano and A Shuen ldquoDynamic capabilities andstrategic managementrdquo Strategic Management Journal vol 18no 7 pp 509ndash533 1997

[19] V A Varma J F Pekny G E Blau and G V Reklaitis ldquoAframework for addressing stochastic and combinatorial aspectsof scheduling and resource allocation in pharmaceutical RampDpipelinesrdquoComputers andChemical Engineering vol 32 no 4-5pp 1000ndash1015 2008

[20] R Kolisch ldquoEfficient priority rules for the resource-constrainedproject scheduling problemrdquo Journal of Operations Manage-ment vol 14 no 3 pp 179ndash192 1996

[21] R Kolisch and S Hartmann ldquoExperimental investigationof heuristics for resource-constrained project scheduling anupdaterdquo European Journal of Operational Research vol 174 no1 pp 23ndash37 2006

[22] M J Leiblein ldquoThe choice of organizational governance formand performance predictions from transaction cost resource-based and real options theoriesrdquo Journal of Management vol29 no 6 pp 937ndash961 2003

[23] G CMoore and I Benbasat ldquoDevelopment of an instrument tomeasure the perceptions of adopting an information technologyinnovationrdquo Information Systems Research vol 2 no 3 pp 192ndash222 1991

[24] R E Hoskisson M A Hitt W P Wan and D Yiu ldquoTheoryand research in strategic management swings of a pendulumrdquoJournal of Management vol 25 no 3 pp 417ndash456 1999

[25] C C Miller and L B Cardinal ldquoStrategic planning and firmperformance a synthesis of more than two decades of researchrdquoAcademy of Management Journal vol 37 no 6 pp 1649ndash16651994

[26] S D Hunt A General Theory of Competition Sage ThousandOaks Calif USA 2000

[27] S Hart and C Banbury ldquoHow strategy-making processes canmake a differencerdquo Strategic Management Journal vol 15 no 4pp 251ndash269 1994

[28] E Drakopoulos ldquoEnterprise network planning and designmethodology and applicationrdquo Computer Communications vol22 no 4 pp 340ndash352 1999

[29] V Kumar B Maheshwari and U Kumar ldquoERP systems imple-mentation best practices in Canadian government organiza-tionsrdquoGovernment InformationQuarterly vol 19 no 2 pp 147ndash172 2002

[30] W R Lewis ldquoStrategic planningrdquo Hospital Materiel Manage-ment Quarterly vol 10 no 4 pp 57ndash63 1989

[31] D Wainwright and T Waring ldquoThree domains for imple-menting integrated information systems redressing the bal-ance between technology strategic and organisational analysisrdquoInternational Journal of InformationManagement vol 24 no 4pp 329ndash346 2004

[32] M Zeleny ldquoOptimal system design with multiple criteria DeNovo programming approachrdquo Engineering Costs and Produc-tion Economics vol 10 no 1 pp 89ndash95 1986

[33] M Zeleny ldquoOptimal given system versus designing optimalsystem the De Novo programming approachrdquo InternationalJournal of General System vol 17 no 3 pp 295ndash307 1990

[34] Y M Zhang G H Huang and X D Zhang ldquoInexact de Novoprogramming for water resources systems planningrdquo EuropeanJournal ofOperational Research vol 199 no 2 pp 531ndash541 2009

[35] M Zeleny ldquoTrade-offs free management via De Novo pro-grammingrdquo International Journal Operations and QuantitativeManagement vol 1 no 1 pp 3ndash13 1995

[36] H Chesbrough W Anhaverbeke and J West Eds Openinnovation Researching a New Paradigm Oxford UniversityPress 2006

[37] J West and S Gallagher ldquoChallenges of open innovation theparadox of firm investment in open-source softwarerdquo R amp DManagement vol 36 no 3 pp 319ndash331 2006

[38] E H Kessler P E Bierly and S Gopalakrishnan ldquoInternalversus external learning in new product development effects onspeed costs and competitive advantagerdquo R amp D Managementvol 30 no 3 pp 213ndash223 2000

[39] O Gassmann and E Enkel ldquoTowards a theory of open innova-tion three core process archetypesrdquo in Proceedings of the RampDManagement Conference (RADMA rsquo04) Lisbon Portugal 2004

[40] L V Kantorovich and T C Koopmans Problems of Applicationof Optimization Methods in Industry Federation of SwedishIndustry 1976

[41] T K Das and B-S Teng ldquoA resource-based theory of strategicalliancesrdquo Journal of Management vol 26 no 1 pp 31ndash61 2000

[42] Z Babic and I Pavic ldquoMulticriterial production planning byDe Novo programming approachrdquo International Journal ofProduction Economics vol 43 no 1 pp 59ndash66 1996

[43] J K C Chen and G H Tzeng ldquoPerspective strategic allianceand resource allocation in supply chain systems through the De

Mathematical Problems in Engineering 13

Novo programming approachrdquo International Journal Sustain-able Strategic Management vol 1 no 3 pp 320ndash339 2009

[44] J-J Huang G-H Tzeng and C-S Ong ldquoMotivation andresource-allocation for strategic alliances through the De NovoperspectiverdquoMathematical and ComputerModelling vol 41 no6-7 pp 711ndash721 2005

[45] J-J Huang G-H Tzeng and C-S Ong ldquoChoosing bestalliance partners and allocating optimal alliance resources usingthe fuzzymulti-objective dummyprogrammingmodelrdquo Journalof the Operational Research Society vol 57 no 10 pp 1216ndash12232006

[46] R M Grant ldquoThe resource-based theory competitive advan-tage implications for strategy formulationrdquo California Manage-ment Review vol 33 no 3 pp 114ndash135 1991

[47] K A Artto and K Wikstrom ldquoWhat is project businessrdquoInternational Journal of Project Management vol 23 no 5 pp343ndash353 2005

[48] J Soderlund ldquoOn the broadening scope of the research onprojects a review and a model for analysisrdquo InternationalJournal of ProjectManagement vol 22 no 8 pp 655ndash667 2004

[49] B Cova P Ghauri and R Salle Project Marketing BeyondCompetitive Bidding JohnWiley amp Sons Chichester UK 2002

[50] T Mandjak and Z Veres ldquoThe D-U-C model and the stages ofthe project marketing processrdquo in Proceedings of the 14th IMPAnnual Conference Proceedings H K Nummela Ed pp 471ndash490 1998

[51] Q Meng Y Huang and R L Cheu ldquoCompetitive facilitylocation on decentralized supply chainsrdquo European Journal ofOperational Research vol 196 no 2 pp 487ndash499 2009

[52] H-M Hsu and W-P Wang ldquoDynamic programming fordelayed product differentiationrdquo European Journal of Opera-tional Research vol 156 no 1 pp 183ndash193 2004

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Mathematical Problems in Engineering

Hindawi Publishing Corporationhttpwwwhindawicom

Differential EquationsInternational Journal of

Volume 2014

Applied MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Mathematical PhysicsAdvances in

Complex AnalysisJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

OptimizationJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Operations ResearchAdvances in

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Function Spaces

Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of Mathematics and Mathematical Sciences

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Algebra

Discrete Dynamics in Nature and Society

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Decision SciencesAdvances in

Discrete MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom

Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Stochastic AnalysisInternational Journal of

12 Mathematical Problems in Engineering

[5] M Zeleny ldquoA case study in multiple objective design DeNovo programmingrdquo inMultiple Criteria Analysis OperationalMethods vol 1 pp 37ndash52 1981

[6] M Zeleny ldquoOptimal system design with multiple criteria DeNovo programming approachrdquo Engineering Costs and Produc-tion Economics vol 10 no 1 pp 89ndash94 1986

[7] S Christophersona M Kitsonb and J Michiec ldquoInnovationnetworks and knowledge exchangerdquo Cambridge Journal ofRegions Economy and Society vol 1 no 2 pp 165ndash173 2008

[8] H W Chesbrough Open Innovation The New Imperativefor Creating and Profiting from Technology Harvard BusinessSchool Press Boston Mass USA 2003

[9] J B Barney ldquoFirm resources and sustained competitive advan-tagerdquo Advances in Strategic Management vol 17 pp 203ndash2272000

[10] B Wernerfelt ldquoA resource-based view of the firmrdquo StrategicManagement Journal vol 5 no 2 pp 171ndash180 1984

[11] T R Holcomb and M A Hitt ldquoToward a model of strategicoutsourcingrdquo Journal of Operations Management vol 25 no 2pp 464ndash481 2007

[12] D J Collis ldquoA resource-based analysis of global competitionthe case of the bearings industryrdquo Strategic Management Jour-nal vol 12 no 1 pp 49ndash68 1991

[13] E Penrose The Theory of the Growth of the Firm John Wileyand Sons New York NY USA 1959

[14] E Anderson andH Gatignon ldquoModes of foreign entry a trans-action cost analysis and propositionsrdquo Journal of InternationalBusiness Studies vol 17 no 3 pp 1ndash26 1986

[15] J Chimhanzi and R E Morgan ldquoExplanations from themarketinghuman resources dyad formarketing strategy imple-mentation effectiveness in service firmsrdquo Journal of BusinessResearch vol 58 no 6 pp 787ndash796 2005

[16] J Fahy ldquoA resource-based analysis of sustainable competitiveadvantage in a global environmentrdquo International BusinessReview vol 11 no 1 pp 57ndash78 2002

[17] M A Harrison R E Hoskisson and D Ireland ldquoSynergies andpost acquisition performance differences versus similarities inresource allocationsrdquo Journal of Management vol 17 no 1 pp173ndash190 1991

[18] D J Teece G Pisano and A Shuen ldquoDynamic capabilities andstrategic managementrdquo Strategic Management Journal vol 18no 7 pp 509ndash533 1997

[19] V A Varma J F Pekny G E Blau and G V Reklaitis ldquoAframework for addressing stochastic and combinatorial aspectsof scheduling and resource allocation in pharmaceutical RampDpipelinesrdquoComputers andChemical Engineering vol 32 no 4-5pp 1000ndash1015 2008

[20] R Kolisch ldquoEfficient priority rules for the resource-constrainedproject scheduling problemrdquo Journal of Operations Manage-ment vol 14 no 3 pp 179ndash192 1996

[21] R Kolisch and S Hartmann ldquoExperimental investigationof heuristics for resource-constrained project scheduling anupdaterdquo European Journal of Operational Research vol 174 no1 pp 23ndash37 2006

[22] M J Leiblein ldquoThe choice of organizational governance formand performance predictions from transaction cost resource-based and real options theoriesrdquo Journal of Management vol29 no 6 pp 937ndash961 2003

[23] G CMoore and I Benbasat ldquoDevelopment of an instrument tomeasure the perceptions of adopting an information technologyinnovationrdquo Information Systems Research vol 2 no 3 pp 192ndash222 1991

[24] R E Hoskisson M A Hitt W P Wan and D Yiu ldquoTheoryand research in strategic management swings of a pendulumrdquoJournal of Management vol 25 no 3 pp 417ndash456 1999

[25] C C Miller and L B Cardinal ldquoStrategic planning and firmperformance a synthesis of more than two decades of researchrdquoAcademy of Management Journal vol 37 no 6 pp 1649ndash16651994

[26] S D Hunt A General Theory of Competition Sage ThousandOaks Calif USA 2000

[27] S Hart and C Banbury ldquoHow strategy-making processes canmake a differencerdquo Strategic Management Journal vol 15 no 4pp 251ndash269 1994

[28] E Drakopoulos ldquoEnterprise network planning and designmethodology and applicationrdquo Computer Communications vol22 no 4 pp 340ndash352 1999

[29] V Kumar B Maheshwari and U Kumar ldquoERP systems imple-mentation best practices in Canadian government organiza-tionsrdquoGovernment InformationQuarterly vol 19 no 2 pp 147ndash172 2002

[30] W R Lewis ldquoStrategic planningrdquo Hospital Materiel Manage-ment Quarterly vol 10 no 4 pp 57ndash63 1989

[31] D Wainwright and T Waring ldquoThree domains for imple-menting integrated information systems redressing the bal-ance between technology strategic and organisational analysisrdquoInternational Journal of InformationManagement vol 24 no 4pp 329ndash346 2004

[32] M Zeleny ldquoOptimal system design with multiple criteria DeNovo programming approachrdquo Engineering Costs and Produc-tion Economics vol 10 no 1 pp 89ndash95 1986

[33] M Zeleny ldquoOptimal given system versus designing optimalsystem the De Novo programming approachrdquo InternationalJournal of General System vol 17 no 3 pp 295ndash307 1990

[34] Y M Zhang G H Huang and X D Zhang ldquoInexact de Novoprogramming for water resources systems planningrdquo EuropeanJournal ofOperational Research vol 199 no 2 pp 531ndash541 2009

[35] M Zeleny ldquoTrade-offs free management via De Novo pro-grammingrdquo International Journal Operations and QuantitativeManagement vol 1 no 1 pp 3ndash13 1995

[36] H Chesbrough W Anhaverbeke and J West Eds Openinnovation Researching a New Paradigm Oxford UniversityPress 2006

[37] J West and S Gallagher ldquoChallenges of open innovation theparadox of firm investment in open-source softwarerdquo R amp DManagement vol 36 no 3 pp 319ndash331 2006

[38] E H Kessler P E Bierly and S Gopalakrishnan ldquoInternalversus external learning in new product development effects onspeed costs and competitive advantagerdquo R amp D Managementvol 30 no 3 pp 213ndash223 2000

[39] O Gassmann and E Enkel ldquoTowards a theory of open innova-tion three core process archetypesrdquo in Proceedings of the RampDManagement Conference (RADMA rsquo04) Lisbon Portugal 2004

[40] L V Kantorovich and T C Koopmans Problems of Applicationof Optimization Methods in Industry Federation of SwedishIndustry 1976

[41] T K Das and B-S Teng ldquoA resource-based theory of strategicalliancesrdquo Journal of Management vol 26 no 1 pp 31ndash61 2000

[42] Z Babic and I Pavic ldquoMulticriterial production planning byDe Novo programming approachrdquo International Journal ofProduction Economics vol 43 no 1 pp 59ndash66 1996

[43] J K C Chen and G H Tzeng ldquoPerspective strategic allianceand resource allocation in supply chain systems through the De

Mathematical Problems in Engineering 13

Novo programming approachrdquo International Journal Sustain-able Strategic Management vol 1 no 3 pp 320ndash339 2009

[44] J-J Huang G-H Tzeng and C-S Ong ldquoMotivation andresource-allocation for strategic alliances through the De NovoperspectiverdquoMathematical and ComputerModelling vol 41 no6-7 pp 711ndash721 2005

[45] J-J Huang G-H Tzeng and C-S Ong ldquoChoosing bestalliance partners and allocating optimal alliance resources usingthe fuzzymulti-objective dummyprogrammingmodelrdquo Journalof the Operational Research Society vol 57 no 10 pp 1216ndash12232006

[46] R M Grant ldquoThe resource-based theory competitive advan-tage implications for strategy formulationrdquo California Manage-ment Review vol 33 no 3 pp 114ndash135 1991

[47] K A Artto and K Wikstrom ldquoWhat is project businessrdquoInternational Journal of Project Management vol 23 no 5 pp343ndash353 2005

[48] J Soderlund ldquoOn the broadening scope of the research onprojects a review and a model for analysisrdquo InternationalJournal of ProjectManagement vol 22 no 8 pp 655ndash667 2004

[49] B Cova P Ghauri and R Salle Project Marketing BeyondCompetitive Bidding JohnWiley amp Sons Chichester UK 2002

[50] T Mandjak and Z Veres ldquoThe D-U-C model and the stages ofthe project marketing processrdquo in Proceedings of the 14th IMPAnnual Conference Proceedings H K Nummela Ed pp 471ndash490 1998

[51] Q Meng Y Huang and R L Cheu ldquoCompetitive facilitylocation on decentralized supply chainsrdquo European Journal ofOperational Research vol 196 no 2 pp 487ndash499 2009

[52] H-M Hsu and W-P Wang ldquoDynamic programming fordelayed product differentiationrdquo European Journal of Opera-tional Research vol 156 no 1 pp 183ndash193 2004

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Mathematical Problems in Engineering

Hindawi Publishing Corporationhttpwwwhindawicom

Differential EquationsInternational Journal of

Volume 2014

Applied MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Mathematical PhysicsAdvances in

Complex AnalysisJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

OptimizationJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Operations ResearchAdvances in

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Function Spaces

Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of Mathematics and Mathematical Sciences

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Algebra

Discrete Dynamics in Nature and Society

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Decision SciencesAdvances in

Discrete MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom

Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Stochastic AnalysisInternational Journal of

Mathematical Problems in Engineering 13

Novo programming approachrdquo International Journal Sustain-able Strategic Management vol 1 no 3 pp 320ndash339 2009

[44] J-J Huang G-H Tzeng and C-S Ong ldquoMotivation andresource-allocation for strategic alliances through the De NovoperspectiverdquoMathematical and ComputerModelling vol 41 no6-7 pp 711ndash721 2005

[45] J-J Huang G-H Tzeng and C-S Ong ldquoChoosing bestalliance partners and allocating optimal alliance resources usingthe fuzzymulti-objective dummyprogrammingmodelrdquo Journalof the Operational Research Society vol 57 no 10 pp 1216ndash12232006

[46] R M Grant ldquoThe resource-based theory competitive advan-tage implications for strategy formulationrdquo California Manage-ment Review vol 33 no 3 pp 114ndash135 1991

[47] K A Artto and K Wikstrom ldquoWhat is project businessrdquoInternational Journal of Project Management vol 23 no 5 pp343ndash353 2005

[48] J Soderlund ldquoOn the broadening scope of the research onprojects a review and a model for analysisrdquo InternationalJournal of ProjectManagement vol 22 no 8 pp 655ndash667 2004

[49] B Cova P Ghauri and R Salle Project Marketing BeyondCompetitive Bidding JohnWiley amp Sons Chichester UK 2002

[50] T Mandjak and Z Veres ldquoThe D-U-C model and the stages ofthe project marketing processrdquo in Proceedings of the 14th IMPAnnual Conference Proceedings H K Nummela Ed pp 471ndash490 1998

[51] Q Meng Y Huang and R L Cheu ldquoCompetitive facilitylocation on decentralized supply chainsrdquo European Journal ofOperational Research vol 196 no 2 pp 487ndash499 2009

[52] H-M Hsu and W-P Wang ldquoDynamic programming fordelayed product differentiationrdquo European Journal of Opera-tional Research vol 156 no 1 pp 183ndash193 2004

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Mathematical Problems in Engineering

Hindawi Publishing Corporationhttpwwwhindawicom

Differential EquationsInternational Journal of

Volume 2014

Applied MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Mathematical PhysicsAdvances in

Complex AnalysisJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

OptimizationJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Operations ResearchAdvances in

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Function Spaces

Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of Mathematics and Mathematical Sciences

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Algebra

Discrete Dynamics in Nature and Society

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Decision SciencesAdvances in

Discrete MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom

Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Stochastic AnalysisInternational Journal of

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Mathematical Problems in Engineering

Hindawi Publishing Corporationhttpwwwhindawicom

Differential EquationsInternational Journal of

Volume 2014

Applied MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Mathematical PhysicsAdvances in

Complex AnalysisJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

OptimizationJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Operations ResearchAdvances in

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Function Spaces

Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of Mathematics and Mathematical Sciences

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Algebra

Discrete Dynamics in Nature and Society

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Decision SciencesAdvances in

Discrete MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom

Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Stochastic AnalysisInternational Journal of