fms

Abstract Global competition, advancements in technology and everchanging customers’ demand have made the manufacturing companies torealize the importance of flexible manufacturing systems (FMS). Theseorganizations are looking at FMS as a viable alternative to enhance theircompetitive edge. But, implementation of this universally accepted andchallenging technology is not an easy task. A large number of articles havebeen reviewed and it is found that the existing literature lacks in providing aclear picture about the implementation of FMS. In this paper, work ofvarious researchers has been studied and it is found that it is really a verydifficult task for any organization to transform into FMS on the basis ofexisting research results. A wide gap exists between the proposed ap-proaches/algorithms for the design of different components of FMS and thereal-life complexities. Besides describing the gap in various issues related toFMS, some barriers, which inhibit the adaptation and implementation ofFMS, have also been identified in this paper.

T. Raj (&)Department of Mechanical Engineering, YMCA Institute of Engineering, Faridabad 121006,Indiae-mail: [email protected]

T. Raje-mail: [email protected]

R. ShankarDepartment of Management Studies, Vishwakarma Bhawan, Indian Institute of TechnologyDelhi, Shaheed Jeet Singh Marg, New Delhi 110 016, Indiae-mail: [email protected]

M. SuhaibDepartment of Mechanical Engineering, Faculty of Engineering & Technology, Jamia MilliaIslamia, New Delhi, Indiae-mail: [email protected]

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Int J Flex Manuf Syst (2007) 19:1–40DOI 10.1007/s10696-007-9015-7

A review of some issues and identification of somebarriers in the implementation of FMS

Tilak Raj Æ Ravi Shankar Æ Mohammed Suhaib

Published online: 9 March 2007� Springer Science+Business Media, LLC 2007

Keywords Flexible manufacturing systems Æ Implementation ÆTransition Æ Adaptation Æ Barriers

1 Introduction

During recent years, the manufacturing organizations are facing manyunpredictable market changes such as shortened product life cycles, techno-logical advancement, intense pressure from competitors, and ever growingcustomers’ expectation for high quality products at a lower cost. The marketconditions are becoming more dynamic and more customers driven. Themanufacturing performance is no longer driven by the product price; insteadother competitive factors such as flexibility, quality, and delivery have becomeequally important (Chan and Swarnkar 2006). Hence, the manufacturers wantsuch type of production technology in which changes can be made withminimum possible time and cost to produce medium to small batches ofproducts. In today’s competitive global market, for their survival, the manu-facturing companies need to be flexible, adaptive, responsive to changes andbe able to produce a variety of products in a short time at a lower cost(Nagalingam and Lin 1999). So, manufacturing flexibility is the most soughtafter property of the modern production systems and such type of flexibilitycan be attained through the adaptation and implementation of FMS.

A flexible manufacturing system is an integrated, computer-controlledcomplex arrangement of automated material handling devices and numeri-cally controlled (NC) machine tools that can simultaneously process medium-sized volumes of a variety of part types (Stecke 1983). This new productionsystem has been designed in such a way that it has the efficiency of a well-balanced transfer line and ability to provide the flexibility of a job shop. FMSis capable of producing a variety of part types and handling flexible routing ofparts instead of running parts in a straight line through machines (Chen andHo 2005). Firms adopt the FMS as a means for meeting the mountingrequirements of customized production (Kumar et al. 2006). It is also viewedas a technology, which can provide a high potential for productivityimprovement in batch production of parts in discrete manufacturing systems.FMS can be very rapidly adjusted to part variety according to the changingmarket demands. It can respond quickly and smoothly to unexpected changesin the market and recently, it is setting new trends in the manufacturing world.It has been hailed as the solution to challenges facing manufacturing industriesworldwide (Parker and Wirth 1999). To continue to meet the challenges of themarketplace, FMS has been acclaimed as the ultimate weapon in boostingproductivity and competitiveness (Sarkis 1997).

While the above arguments support the adoption of an FMS, its adoption isnot an easy job. Diffusion of FMS, in specific and advance technology, is farfrom easy (Bellasi and Fadlalla 1998). It is generally found that industries,which claim to have adopted the FMS environment, use only a partial FMS.Industries face different types of problems while planning to adopt FMS.

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Many research papers have been published which reveal several types ofmethodologies for installation and control of FMS. But practically thesetechniques and methodologies have not been found to be suitable by theindustries especially in developing countries. It has been noticed that indus-tries in USA, Japan, Australia, UK, and Europe have responded to FMS tosome extent but the response of industries in developing countries is verymeager. Industries willing to adopt FMS have to struggle with a variety ofproblems. The purpose of this paper is to identify and study some of theinvolved barriers, which are critical during transition to FMS.

The organization of the rest of the paper starts with a review of the generalprocedure for implementation of FMS and shortcomings in this area.Different issues related to implementation of FMS are then listed and dis-cussed. Some important barriers, which inhibit the implementation of FMS inreal-life scenario, are identified and listed. A summary of this work andconclusions are discussed in the last section.

2 Implementation procedure for FMS

Several authors have presented articles, which describe various steps neces-sary for the implementation of FMS technology (e.g., Fry and Smith 1989;Primrose and Leonard 1991; Groover 2003; Rezaie and Ostadi 2007). Fry andSmith (1989) have described a detailed procedure for the implementation ofFMS in Department of Defence of U.S. They have proposed the followingeight distinct stages in the implementation of a flexible manufacturing system,i.e., (i) identify the manufacturing requirements of the parts to be produced,(ii) identify and evaluate the alternative technologies, (iii) choose theappropriate technology, (iv) send out requests for proposals, (v) evaluate andselect the vendor, (vi) installation of FMS, (vii) system configuration, and(viii) shake down the system. Primrose and Leonard (1991) have suggestedthat the implementation procedure for flexible technology can be divided intothree steps, i.e., (i) investment appraisal, (ii) technology selection and (iii)technical installation. Groover (2003) has also discussed FMS planning andimplementation issue. He has proposed that this issue should be divided intwo phases, i.e., (i) FMS planning and design phase and (ii) FMS operationalphase. For the first phase, he has proposed the consideration of part family,processing requirements, physical characteristics of the work parts, productionvolume, types of workstations, variations in process routings and FMS layout,material handling system, work-in-process inventory, cutting tools and palletfixtures. For handling the second phase, he has proposed that operationalproblems must be solved through proper scheduling and dispatching, machineloading, part routing, part grouping, tool management and pallet and fixtureallocation. Rezaie and Ostadi (2007) have introduced a dynamic programmingmodel to analyze the optimal and phased implementation of flexibletechnology in a manufacturing system.

Issues and barriers in the implementation of FMS 3

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2.1 Discussion on research related to FMS implementation

With the study of other articles on the same issue, it is found that no clear-cutprocedure for the implementation of FMS has been proposed. Instead, manycomplex techniques like expert systems, artificial intelligence and neuralnetwork, etc., have been experimented and suggested for the implementationand integration of different components of FMS, which are so complex that itis often infeasible to apply them in the real-life implementation of FMS. Theexisting research work is found to be handicapped in fully answering thefollowing questions, which are generally asked by the manufacturing man-agers in the industry for a possible transition of their traditional manufac-turing system to FMS:

• What types of technologies, standard industrial networks and protocols areavailable for FMS system integration and how are they managed?

• What types of software, sensors and other mechatronic components areavailable for the system integration? What are their related constraints andhow are they managed in realistic situations?

• How will the unmanned operations of FMS be managed, especially in athird shift? How will the problems related to tool management andmaintenance be looked after in the third shift?

• How quickly can one shift to FMS? If a typical traditional company wantsto adopt FMS, then, how much time is needed for its transition?

• How much gain in profits (i.e., economical efficiency), flexibility, auto-mation and productivity, etc., can be expected over the time horizon? If adecision for FMS installation is taken, then (i) how much time is needed toachieve the breakeven, (ii) when will the profits start pouring in, and (iii)how will the new production system be successful in a long run?

• How much flexibility will be achieved? If a new random part is to bemanufactured in FMS on an urgent basis and a sudden design change isrequired in the product configuration according to market demand, then,how quickly can it be handled in the FMS?

• How will pallets and fixtures handle a variety of parts? Groover (2003) hassuggested the use of a number of fixtures for different parts. No doubt,different pallets and fixtures can be utilized and procured for a number ofparts, but (i) how much setting time will be required for different palletsand fixtures, (ii) for how much time will the system be idle, (iii) how manyparts can a single pallet or fixture handle, (iv) if some how flexible fixturesare designed, then what about their cost and complexity, (v) if flexiblefixtures are not available then, what will be the difference between an FMSand a simple automated production system, and (v) where is the flexibilityin the system in such a case?

• Generally, there is a scarcity of vendors supplying all the components ofFMS, i.e., there are different manufacturers of computer numerical control(CNC) machines, robots, automatic guided vehicles (AGVs), co-ordinatemeasuring machines (CMMs), conveyors, automatic storage and retrieval

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system (AS/RS), etc. In developing countries, there is even scarcity ofmanufacturers of robots and AGVs. When these hardware components arepurchased from different vendors, then what is the best way for theintegration of these components?

• What about system maintenance and its up-gradation in a long run?• What about training and upgrading the skills of engineers and concerned

staff in the area of FMS in a country where these systems have not beenintroduced?

We believe that these questions are really irritating and these have not beenproperly addressed by the researchers. A dedicated research work is stillpending for finding the solution to the real-life problems related to planningand implementation of FMS. Following sections illustrate more gap areaswithin different issues related to FMS.

3 Issues related to implementation of FMS

Based on the published research papers regarding planning, design andimplementation of FMS and discussion with the production managers inindustries and with academicians, the following issues are discussed here:

• Issue regarding loading of parts in FMS• Issue regarding scheduling techniques• Issue regarding material handling• Issue regarding flexibility and its measurement• Issue regarding machine tools• Issue regarding operation, control and maintenance techniques• Issue regarding human element and culture

3.1 Issue regarding loading of parts in FMS

Loading involves decision about the assignment of work to different machinetools in the manufacturing system for the purpose of machining. Character-istics of different components of FMS and socio-technical environment, wherethe FMS operates, deeply affect the loading problem. Components of FMS,e.g., types of machine tools, control system of FMS, cutting tools and theirhandling and storage systems and tool magazine capacity, etc., put differenttypes of constraints which must be considered while deciding and formulatinga loading plan for FMS.

A loading problem is strongly affected by the types of machine tools andtheir availability in the FMS. CNC machines are more versatile and precise inperforming different operations in comparison to conventional machine tools.Besides this, they can be utilized in unmanned conditions also. So, beforeformulating a loading problem, one should consider the nature and capacity ofall the machine tools involved in FMS. Normally, each machine of the FMS


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undergoes through a preventive maintenance plan as per the instructions ofthe machine tool manufacturer. Therefore, while following the schedule of themaintenance team of the plant, some machines or the whole FMS may beunavailable for particular periods of time. This introduces a constraint at theloading level on the capacity of machine.

The behavior of an FMS is affected by the characteristics of its controlsystem. At present, the control system of most of the existing FMSs is basedon a supervisor, coordinating the behavior of various devices (machines,transport system, etc.) and on a set of computerized numerical controls(CNCs) and programmable logic controllers (PLCs), each of which controls aspecific device. The supervisor is the proprietary software running on a centralcomputer. A loading problem can be defined as ‘‘... given a set of parts to beproduced, set of tools that are needed for processing the parts on a set ofmachines, and using a set of resources such as material handling systems,pallets and fixtures, how should the parts be assigned and tools allocated sothat some measure of productivity is optimized...’’(Kumar et al. 2006). Eversince, the first article written by Stecke and Solberg (1981) on the production-planning problem of FMS has been published, a lot of research has beendevoted in this area by various researchers. Among the different levels of theproduction planning hierarchy proposed by Stecke (1983), the loading prob-lem has received considerable attention. It is very important to organize thepast research in this area before proceeding to identify the gaps betweendocumented research and the actual real-life requirement of its industrialapplication. With a detailed-study of literature, it has been found thatmachine-loading problem is addressed by following approaches:

(a) Analytical and Mathematical programming-based methods. The pio-neering work in this area described the FMS planning problem into five sub-problems i.e. (i) part selection, (ii) resource allocation, (iii) machine grouping,(iv) production ratio determination and (v) loading; and formulated these asnon-linear 0–1 mixed-integer programming (MIP) problem (Stecke and Solberg(1981); Stecke (1983)). Stecke (1986) improved her earlier work (Stecke 1983)by proposing a new non-linear MIP formulation. Sarin and Chen (1987)developed another mixed integer-programming model to determine routing ofparts through machines and to allocate proper cutting tools for achievingoverall minimum cost. Lashkari et al. (1987) added the aspects of refixturingand limitations on the number of available tools to the formulation of theoperations allocation problem given by Stecke (1983). These formulationsassumed that a product-mix problem was already solved and therefore limitthe models to be suitable only for dedicated FMS (Mukhopadhaya et al.1992). Lee and Jung (1989) proposed that the production phase of an FMSmust take into account different objectives at the same time. Therefore, theyintroduced a multiple objective problem and tackled it by means of goalprogramming. Chen and Askin (1990) also discussed the multi-objectiveevaluation of FMS loading heuristics. Kumar et al. (1990) tried to find out ajoint solution to the machine grouping and loading problem. Sawik (1990)proposed a hierarchy for the production-planning task based on part type

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selection, part input sequencing, machine loading and operational scheduling.Chen and Chung (1991) discussed the effects of loading and routing decisionson the performance of FMS. Kouvelis (1991) described a two-level hierar-chical scheme for determining optimal number of tools of each type requiredfor efficient operation of manufacturing system over a planning horizon onminimum cost basis. At the first level, a long-term operations assignment tomachines is specified and at the second level, the optimal tooling decision ismade. Akturk and Avci (1996) proposed a non-linear MIP solution method-ology for simultaneously determining the optimum machining conditions andtool allocation to minimize the production cost of a multiple operation casewhere alternate tools can be used for each operation. Atmani and Lashkari(1998) presented a linear 0–1 integer-programming model for machine toolassignment and operation allocation in FMS. This model determines anoptimal plan by minimizing the total costs of operations, material handlingand setups. Gamila and Motavali (2003) discussed the problems of partloading, tool loading and part scheduling. They developed a mathematicalmodel, using a 0–1 MIP technique, to select machines and assign operationsand the required tools to the machines in order to minimize the summation ofmaximum completion time, material handling time and total processing time.

(b) Heuristic-based methods. Stecke and Tallbot (1983) reported that themodels proposed by Stecke (1983) may not be practical for real time appli-cations because these models will require a great amount of computationalefforts and therefore, large computer time in finding the solutions to real lifeproblems. Hence, they proposed heuristic algorithms for minimizing the partmovement and balancing the load on machines of equal size. Shanker andSriniwasulu (1989) suggested three heuristic-based approaches for solvingloading problems in case of random FMS. They proposed these heuristics forthe problems having bi-criteria objectives. In the first heuristic, the mainobjective is to minimize the workload unbalance and in the other two, theobjective is to maximize the throughput. Mukhopadhyay et al. (1992) sug-gested a heuristic based approach to solve the loading problem in FMS bydeveloping the concept of essentiality ratio for the objectives of maximizationthroughput time of the system and minimization of the system unbalance.Tiwari et al. (1997) used a pre-determined job-sequencing rule as an input totheir proposed heuristic. They developed a Petri-net model for the proposedheuristic for delineating its graphical representation. Some researchers haveutilized meta-heuristic approaches like tabu search and simulated annealing(SA) for operation allocation on different machines (Kim and Yano 1993;Tiwari et al. 1997; Tiwari and Vidyarthi 2000; Sarma et al. 2002; Swarnkar andTiwari 2004). But these approaches sometimes give infeasible solutions due todifferent restrictions on processing times and non-availability of tool slots.

(c) Genetic Algorithm (GA)-based methods. GA-based algorithms have agreat potential to solve machine-loading problems of FMS. These arestochastic search techniques that rely on the process of natural solution(Goldberg et al. 1989). Although GA is a global search technique, its practicalusefulness lies in its capability to handle the problem constraints. Kumar and


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Shanker (2000) solved the machine loading and part type selection problemby using GA methodology. Many other researchers have also developedGA-based heuristic for the different objectives such as minimization ofthroughput time, system unbalance, etc. (Mori and Tseng 1997; Guvenir andErel 1998; Cormier et al. 1998; Eatson and Mansour 1999; Tiwari and Vid-yarthi 2000; Liaw 2000; Cai and Li 2000; Bortfieldt and Gehring 2001; Kumaret al. 2006).

(d) Simulation-based methods. When a system cannot be evaluated ana-lytically to get a single solution then simulation may be a good option.Computer simulations provide almost complete and detailed behavior of thesystem, especially with respect to comparative analysis of solution variants, bywhich the best solution can be reached. In simulation-based methods, asymbolic representation of real world is coded into a computer program toevaluate the problems (Kovacs 1992).

Stecke and Solberg (1981) carried out a simulation-based study of a dedi-cated FMS in which five loading strategies were tested against sixteen dis-patching rules. Their study indicated that the choice of the applicable loadingstrategy depends upon many variables particular to a system. As their studywas based on a dedicated type of FMS, the loading strategies were simply theprocedures to allocate the operations to a number of same types of machines.Kost and Zdanowicz (2005) have presented the problems involving identifi-cation, modeling and simulation of the operations of FMS in their study. Theyhave suggested that in order to carry out the simulation in a correct way, onemust know the precise duration time of particular operations, both involvingmachining and transportation. But at the modeling stage it is almost impos-sible to know the precise durations, mainly of transport operation. These maybe defined only after the start-up of the system and after measuring them inreal conditions. The works of both Kovacs (1992) and Kost and Zdanowicz(2005) contradict each other. Kovacs (1992) suggests that modeling is the bestway of finding an optimal solution in the design of FMS but at the same timeKost and Zdanowicz (2005) propose that modeling cannot represent a real-lifeFMS problem. Few simulation-based methods have also been formulated byMukhopadhyay et al. (1998) and Zolfaghari and Liang (1999).

3.1.1 Discussion on research related to FMS loading

Though analytical and mathematical programming-based methods, e.g., inte-ger programming, dynamic programming, branch and bound methods, etc. arerobust in applications yet they tend to become impractical when problem sizeincreases. A wide range of multi-objective problems has been addressed bydifferent researchers. However, in such type of problems, some objectives turnout to be conflicting in various situations. In real-life industrial applications,number of variables and constraints increases many times and it becomes evenimpossible to satisfy all the goals at a time.

The MIP approach has been found to be computationally infeasible evenfor deterministic formulations. Moreover, even for moderate-sized FMSs, the

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computation time required for MIP approaches is considerably large.Heuristic approaches are generally based on rules and rely, basically, onempirical data. Hence, main limitation of heuristic approaches is theirinability to estimate the results in a new or completely changed environment.Though simulation is an effective technique for dynamic analysis, but for FMSdesign, it lacks the ability to provide an optimal solution. In many cases,optimization of the loading problems in FMS is heavily influenced by thespatial configuration of the system. The simulation techniques have anotherlimitation, i.e., more number of iterations is needed to find the best possiblesolution and there is always a possibility of returning to some recently foundsolutions which leads to non-optimized solutions.

Most of the researchers have considered the loading problems with theconstraints related to cutting tools (e.g., tool magazine capacity, limitednumber of tool copies, limited tool life, presence of all the tools on themagazine, elimination of infeasible couples like tool and machine or operationand machine) and availability of machining time. Very few researchersincluded the constraints related to pallets/fixture allocation, surface roughnessand operation completion time. The consideration of constraints related tocutting tools and tool magazine increases the complexity of problem formu-lation especially in case of non-linear terms of Stecke (1983). The problemsincluding constraints of pallets or fixture allocation have emphasized on theoperations of a part while paying no attention to the types of machinesinvolved in the operations. But the consideration of the type of machine is animportant issue as it can influence the major objectives in a big way; hence itneeds proper attention of researchers.

Another problem in some of the previous works related to part selectionand machine loading is that machining speed for each job-tool, machinecombination has been treated as a pre-determined constant. However, it isunrealistic to find a machining speed for an unknown job-tool-machinecombination.

It is evident from the above discussion that all the four types of approachesare handicapped in producing an optimal solution to loading problems ofFMS. Though good attention has been paid to machine loading problems, but,most of these studies focus on modeling the problems and emphasis has beenput on the optimization of certain performance measures rather than onunderstanding the problem and finding the interaction between the differentfactors in the system.

Researchers have proposed many methodologies for solving the loadingproblems of FMS but in the end, majority of them have shown the limitationsof their own work in the real-life situations, e.g., Stecke(1983) and Atmani andLashkari (1998). Stecke (1986) has used a MIP approach for solving themachine loading and grouping problems of FMS and presented the solutionwith the help of large computer (CDC 6600) by using a standard mixed integercode but she concluded her work with the statement: ‘‘...real-time FMS controlrequires very quick solutions to these planning problems. Many companies donot have a mixed integer code available.’’ It is clear that the hierarchical


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approach was developed for a particular case-company and could not be ap-plied to a general case. She has further suggested the development of efficientheuristic algorithms for providing good solutions to machine loading andgrouping problems of FMS. Atmani and Lashkari (1998) conclude their worklike this: ‘‘The real-life, industrial application of this model will invariablyresult in large numbers of variables and constraints which may over-run thecapabilities of the integer programming software currently available. Underthese conditions the use of heuristic solutions procedures is recommended.’’

Most of the researchers have used the objectives of minimizing the totalmachining time, machining cost, inventory cost, throughput time, minimizationof makespan and frequency of tool movement for the loading and schedulingproblems. But they have neglected the set-up cost, machining cost and themovement of parts between the machines. Use of these criteria, generally,creates imbalance in the machine workload and reduces machine utilization.Due to unbalanced workloads and unmatched production resources in FMS,slack time is inevitable and it is not negotiable. But this slack time has not beengiven due consideration in machine loading problems. Grieco et al. (2001)presented a wide review of different approaches to the FMS loading problemsand in the end of their work, they commented like this: ‘‘Analysis of the lit-erature on loading shows that very few articles deal with the same loadingproblem. It results in the fragmentation of research effort. This situation is madeeven more critical by the fact that few articles present detailed real or realistic testcases, which can be adopted as test beds for subsequent research work.’’ Acommon complaint of industrial managers is that theoretical approaches toFMS loading problems fall short of realism. The real life, industrial applica-tions of above models will invariably result in a large number of variables andconstraints, which may put a question mark on the real adaptation of thesetechniques, and it is found that following questions still remain unanswered:

1. What types of characteristics are desired in production planning hierarchyfor deciding the loading module?

2. What is the integration of loading module at different levels of productionplanning hierarchy?

3. What is the best way to meet the due dates?4. What is the effect of characteristics of plant where the FMS operates on

the loading model?5. How is the proposed loading model affected by (i) the number of shifts

and their characteristics, (ii) flow of tools in the tool room, (iii) policiesadopted for preventive maintenance, and (iv) arrival of new parts?

6. What is the effect of tool life management, especially in case of unmannedshifts, for finding the full production capacity of FMS?

From above discussion, it becomes clear that some attention is required tomake the proposed loading models applicable to real-case situations. Thisimportant area needs further exploration and can be addressed in futureresearch work.

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3.2 Issue regarding scheduling of parts in FMS

Scheduling is the allocation of resources over time to perform tasks (Baker1974). This is a well-known and very general definition of scheduling. Thepurpose of scheduling is to determine when to process which job and by whichresource so that production constraints are satisfied and production objectivesare met (Yu et al. 2003). In view of scheduling theory, a general FMS may beconsidered to be job shop with parallel machines and additional limitedresources. There are, however, substantial differences between a conventionaljob shop and an FMS where one should additionally consider such FMSfeatures as alternate routings, pallet and fixture limitations and finite-in-pro-cess buffers (Sawik 1990). In classical scheduling environments, only oneresource type is considered (the machine) and the basic scheduling problemreduces to deciding the allocation of this resource type to process the jobs.The FMS environment is quite different in many respects from the classicalmachine shop. The main difference of scheduling between a conventionalproduction system and a flexible manufacturing system is caused by flexibilityand automation in a modern manufacturing environment. In an FMS, onemachine can process various different parts and one operation can be pro-cessed in various different machines. These flexibilities increase significantlythe feasible solution space and complicate the FMS scheduling (Bing 1998).

The increasing automation and complexity of FMS requires that an effec-tive and improved scheduling technique should be developed for it. Thesuccessful development of realistic scheduling techniques for FMS could havea significant effect on manufacturing industry. Liu and Maccarthy (1996) haveidentified and discussed five major factors influencing the FMS schedulingproblems such as (i) system type such as a single flexible machine (SFM), aflexible manufacturing cell (FMC), a multi-machine flexible manufacturingsystem (MMFMS) and a multi-cell flexible manufacturing system (MCFMS),(ii) capacity constraints, (iii) job characteristics, (iv) production managementenvironment and (v) scheduling criteria.

3.2.1 Scheduling techniques in FMS

A review of literature indicates that there are hundreds of articles on sched-uling problems proposed by different authors at different times. On the basisof study of some of these articles, the approaches used for scheduling prob-lems can be divided in four categories, as follows:

(a) Analytical and Mathematical programming-based methods. Stecke(1983) applied mathematical programming techniques to solve various FMSplanning and machine loading problems and in the same article, she suggestedthat large problems couldn’t be effectively handled with the proposed meth-odology. Chang et al. (1984) proposed a two-phase approximate methodologyfor quasi-real-time scheduling of FMS. In this methodology, phase-I utilizesthe reduced enumeration to generate a number of schedules and phase-II isutilized in real-time to select the optimal schedule for a particular schedule.


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Although authors have proved that this approach performs better than thetraditional job shop scheduling heuristics yet their work does not comment onthe computational efforts required for the formulation of integer program-ming model for second phase.

Chan (1999) has studied the effectiveness of operational control rules inscheduling an FMS with the use of computer simulation. He has proposedthree control rules: (1) dynamic alternative routing (DAR), (2) plannedalternative routing (PAR) and (3) no alternative routing (NAR) for theselection of alternative routing for each part and applied these rules to FMSwith either local buffers with infinite capacity or without local buffers. He alsoexamined the influences of two types of loading stations, i.e., universal loadingstation and dedicated loading station on the performance of FMS. A hypo-thetical model of FMS has been considered with some basic assumptions like(i) demand for each part is known and (ii) the traveling time of AGVsbetween stations is insignificant, when compared to processing times. Theresults of this study are insignificant in real life situations where part demandis variable and traveling time of AGVs is highly significant.

Sawik (1990) has formulated the production scheduling of an FMS as amulti-level integer program. He has proposed a hierarchical decision structurethat includes the problems of (1) part type selection, (2) machine loading, (3)part-input sequencing, and (4) operation scheduling. The author has presentedthe integer programming formulation for all these problems as well as algo-rithm for part selection and operation scheduling. Though the proposedalgorithm provides a period-by-period production schedule, which can be usedon-line as well as off-line, yet some important characteristics of FMS liketransportation time and capacity, buffer limitations, etc. have not been in-cluded in the proposed algorithm, which makes it non-realistic for practicalpurposes. Some other analytical approaches have also been formulated byresearchers (Nasir and Elsayad 1990; Ghosh and Gaimon 1992; Sodhi et al.1994; Dolinska and Besant 1995; Jinyan et al. 1995; Rajamani and Adil 1996;Das and Nagendra 1997; Guerrero et al. 1999; Chan 1999; Saygin and Kilic1999).

(b) Heuristic-oriented methods. A large number of mathematical modelshave been developed for solving the scheduling problems in FMS. But, mainlimitation of these models is that large amount of calculations are needed forfinding the feasible solution. This makes the mathematical approachesinfeasible in realistic conditions. Hence, in most of the cases, these formula-tions have been used as a basis for developing the heuristics for FMS sched-uling.

Abdin (1986) developed a heuristic for the scheduling problem in FMS withalternate machines selection for an operation. Lee and DiCesare (1992)developed a heuristic based Petri-Net model, which could handle complexitiesand uncertainties in FMS environment such as rescheduling and routingflexibilities. Zhao and Wu (2001) have suggested a genetic algorithm (GA)based heuristic to solve the job-sequencing problem of FMS with multipleroutes. This means that parts, of all types, can be processed through

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alternative routings and there can be several machines for each machine type.For solving these general scheduling problems, they have proposed a geneticalgorithm approach and have introduced the concepts of virtual and realoperations. Concepts of flexible-routing scheduling problem, which involverouting selection, machine selection and processing sequence selection, areintroduced. Chan et al. (2003) have supported this fact that FMSs are moresensitive to system disturbances and they require an immediate response tochanges in the system states. This can be achieved by real time dynamicscheduling. For this purpose, they developed an algorithm, which is based onpre-emptive approach. They proved that system’s performance when usingdynamic scheduling could be improved by changing the corresponding dis-patching rule at a correct frequency. But, main drawback of their proposedalgorithm is that they have considered only machine dispatching rules andother important operation rules such as AGV dispatching, machine selectionrules, tool selection rules, etc. have not been taken into account. These rulesare very important as far as the dynamic nature of FMS is concerned. Manyother researchers have also developed heuristic-based approaches for FMSscheduling such as Widmer (1991), Fanti et al.(1992), Van Laarhovenet al.(1992), Barness and Chambers (1995), Sakawa et al. (1996), He et al.(1996), Bryne and Chutima (1997), Hertz et al. (1998), Ecker and Gupta(2005) and Low et al. (2006).

(c) Expert system-based methods. Shaw and Whinston (1989) havedescribed a scheduling approach, which employs a knowledge-based system tocarry out the non-linear planning method developed in artificial intelligence.A prototype of this scheduling system has been applied to solve the schedulingproblem in FMS. This scheduling method is characterized by its knowledge-based organization, symbolic representation, state-space inference, and itsability for dynamic scheduling and plan revision. It provides a foundation forintegrating intelligent planning, scheduling and machine learning in FMSs.Chandra and Talvage (1991) and Naidu and Vishwanandham (1992) havedeveloped and proposed intelligent systems consisting of a rule-base foralternate machine selection. Dadone et al. (1997) have proposed a fuzzy logicsystem (FLS) and fuzzy multiple attribute decision making (MADM) tech-niques for scheduling of FMS. It is the authors’ opinion that the importance ofcommon sense and human expertise in scheduling, together with fuzzy logicability to mimic human reasoning, along with the ease of dealing linguisticvariables makes it a very suitable and powerful tool for scheduling in an FMS.But in the proposed model, tool management and failure of workstations and/or transport systems have not been considered which affect the industrialfeasibility of fuzzy scheduler.

Sharafali et al. (2004) have considered the problem of production sched-uling in an FMS with stochastic demand. They have proposed a model similarto polling model with the objective of minimizing the total average cost. Apolling model is a multiple channel queuing system in which queues are servedin a cyclic or some other predetermined order by a single server. Based ontheir findings with this model, they have proposed that in FMS a part family


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can be mixed with other part family and wastage of production time can bereduced. Sallez et al. (2004) have proposed a product-based heterarchical(PBH) approach for the dynamic scheduling of FMS. Authors have shownthat specific advantage is its robustness and adaptability to its environment.However, a serious drawback of the proposed approach is that it becomesinfeasible in case all the entities of the system are independent.

(d) Simulation-based methods. Chang et al. (1984) examined various dis-patching rules for dynamic scheduling in a flexible manufacturing system withthe use of simulation programs. The main problem with the simulation-basedmethods is that it does not provide any solutions but it only tests the resultingperformance so that the best heuristic rule for a given system can be selected.Montazeri and Van Wassenhove (1990) also reviewed the performance ofdifferent dispatching rules for simulation model of an FMS. They concludedthat dispatching rules have a good impact on the different performancecriteria, e.g., makespan, machine utilization and buffer utilization. But, gen-eral results are not available as the performance of scheduling rules not onlydepends upon the selected criteria but also on the configuration of the pro-duction system. Harmonosky and Robohn (1995) identified some factors,which can influence the implementation time of simulation as a real-timecontrol tool. Weintraub et al. (1999) proposed simulation-based schedulingdecisions in minimizing the maximum lateness of large-scale system.

Jain and Foley (2002) conducted a simulation study aimed at understandingthe impact of an interruption on a schedule in order to build a knowledge basefor intelligent selection of a response from a set of alternatives and identifiedsignificant major factors and their interactions. They have reported that theimpact of interruption is more disruptive in typical FMS than in conventionalflow shop environments and the impact of an interruption at a bottleneckmachine is much higher than an interruption at a non-bottleneck machine.

It is found that most of the work related to scheduling problem of FMS hasbeen dedicated to static conditions which does not take into account theunexpected problems such as arrival of rush orders, machine breakdowns,non-availability of tools, AGV problems, etc. These problems are very much apart of the practical operation of FMS and render the existing scheduleinfeasible. Shi-jin et al. (2006) have also supported this argument and haveproposed a filtered-beam-search (FBS)-based heuristic algorithm to solve thedynamic scheduling problem in a large job shop FMS environment withrealistic disturbances. They have evaluated the performance of the proposedalgorithm with only computational experiments. At the end of their proposedstudy, they are doubtful about the effectiveness and robustness of thescheduling system with this algorithm and its implementation in real life cases.

3.2.2 Discussion on research related to FMS scheduling

The mathematical programming models provide an understanding of theproblem structure and the factors contributing to the complexity and growthin problem size. They may be used in testing and comparing new solution

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methods. But, the main limitation of these models is due to heavy computa-tions requirement for finding a solution for the target problems. This com-putational complexity makes these methodologies impractical for real-timecontrol in most of the applications. Another problem with the existingmethodologies based on analytical mathematical programming is that they aregenerally off-line techniques and their ability is limited in handling the real-life manufacturing environment. It is because of their failure to handle therealistic and dynamic behavior of FMS and lack of providing the solutionswithin the reasonable time period. In addition to above cited limitations,another shortcoming of analytical approaches is that, the constraints on thestorage of work-in-process inventory and on the material handling devicessuch as AGVs, robots, etc., have not been included in most of these models.Both of these constraints are very critical for the real-time implementationand design of FMS scheduling techniques.

Several heuristic methods based on branch and bound, genetic algorithms,simulated annealing, tabu search and Langrangean relaxation approacheshave been developed for FMS scheduling problems. But main drawback ofsuch methods is that they require an initial solution and the quality of finalsolution depends upon the initial solution. Smith et al. (1993) have stated,‘‘unfortunately, increased importance of scheduling FMS does not impact thecomplexity of the scheduling problem. Also, limited inventory buffers, fastermachine setup times, more alternate routings and the existence of toolingconstraints all serve to complicate the FMS scheduling further.’’

Scheduling in an FMS environment is more complex and difficult than inconventional manufacturing environment. This is primarily due to the usageof versatile machines, which are capable of performing many different oper-ations resulting in many alternative routes for part types. Another problemwhich has been noticed in the existing literature is that, in most of thescheduling problem formulations, set-up time has been assumed to be negli-gible or a very small portion of processing time. But, in realistic conditions,set-up time reduction is the main expectation from the flexible manufacturingsystem and cannot be neglected as such.

There is an utmost need that research communities must, now, focus ondeveloping some practical methods, which quickly solve the real-worldscheduling problems because no perfect solution has yet been found for theseproblems due to the complexity of FMS environment. There are someimportant factors, which make the scheduling problem very complex such as:

• There are varieties of part families to be produced in FMS.• At any time of production, a mix-part family may be loaded for production

in FMS.• A part may have many attributes.• Different part types share common tools (Sharafali et al. 2004).• An optimal solution requires simultaneously partitioning the production

order into the minimal number of batches.


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• Basic purpose of FMS is to produce a variety of products, which requireconfigurable or similar type of tool changing devices as well as transpor-tation and storing devices. But in real life cases, production managers maytry to avoid this situation.

Form the above discussion it can be concluded that though a large numberof research papers have appeared in the field of FMS regarding its schedulingproblems but most of them are based on either static conditions or theirsolutions are referred to particular cases only. Researchers have not ade-quately considered the real-life scenario, which is dynamic in nature andrequires such type of scheduling methodologies, which are robust and effec-tive. Following gaps, still, exist between the reported research and the real-lifescheduling of FMS:

1. In most of the studies, the impact of product variety has not been ade-quately studied for real-life applications.

2. The impact of uncertainties such as machine breakdown, shortage of tools,shortage of materials, sudden rush of orders and quality failures have notbeen given due consideration in the preparation of schedulers.

3. Constraints related to material handling devices like AGVs, i.e., their loadcarrying capacity, flexibility in handling a variety of parts and routingflexibility, etc., have not been paid proper attention in most of the studies.

4. In most of the cases, there is no integration between process planning andproduction planning which leads to suboptimal solutions of the schedulingproblems.

3.3 Issue regarding material handling in FMS

An FMS consists of a set of machine tools and a material handling system(MHS) linked by a network of computers controlling and interfacing withthem. Unlike the traditional MHS, where a human element is involved in thetransportation of materials between various locations, human intervention isalmost non-existent in FMS (Mahadevan and Narendran 1990). This has beenmade possible by developments in guided-vehicle technology and computer-controlled MHSs. Among the various equipments employed for materialhandling in FMS, automated guided vehicles (AGVs) have been the mostpopular choice. An automated guided vehicle system (AGVS) featuresbattery powered, driverless vehicle moving on a guided path layout.

No human intervention is needed for the guidance, steering or control ofsuch vehicles. An off-board controller is used to send dispatcher commandsfor the identification of the load, destination of the load and other instructionsrelated to loading and unloading of the load. It has programming capabilitiesfor path selection and can be reconfigured easily to accommodate changes inproduction volume, product mix, product routing and equipment interfacingrequirements. These flexibilities are essentially required in an FMS environ-ment (Rajotia et al. 1998).

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3.3.1 Design issues for AGVs

The power and smaller size of microprocessors have given guided vehicles thecapability of operating autonomously, even in complex transport applications.The design of a material handling system using AGVs must address the fol-lowing issues (Mahadevan and Narendran 1990; Vis 2006; Shankar and Vrat1999):

1. The number of vehicles required.2. The layout of the AGV tracks in the shop.3. Decision regarding provision of control zones and type, number and

capacity of buffer for the vehicles.4. Vehicle dispatching rules.5. Traffic management: prediction and avoidance of collisions and dead-

locks.6. Positioning of idle vehicles.7. Battery management.8. Failure management.

Several authors have contributed in the area of design issues of AGVs.Some of the previous researches in this area are discussed here. Maxwell andMuckstadt (1982) studied the problem of the design of an AGV system. Theydid pioneering work in analytical modeling of operational features of an AGVsystem. In an environment comprising primarily of assembly operations forfinished products, they proposed a time-independent model to estimate theminimum number of vehicles required to support the material handling needs.A standard transportation problem was formulated which assigned emptyvehicle trips between various stations to minimize the total empty vehicletravel time. Egbelu and Tanchoco (1984) examined the effects of some heu-ristic rules for dispatching AGVs in a job shop environment. Their studyconsidered the case where the volume of material flow was large. Later on,Egbelu and Tanchoco (1986) analyzed the possibility of bi-directional flow inguided paths. Based on a simulation analysis, they concluded that bi-direc-tional flow increases the throughput, especially in systems that require only asmall number of vehicles. Bozer and Srinivasan (1989) suggested a tandemconfiguration for reducing software and control complexity of AGV-basedMHS. Ozden (1988) conducted a simulation study of a small FMS to considersimultaneously design parameters such as traffic pattern, number of AGVs,the load carrying capacity of each AGV and queue capacity of the machines.

Malmborg (1990) suggested a scheme for computing travel time of emptyvehicle, which is opposite to the approach taken by Maxwell and Muckstadt(1982). Important differences arise on two counts—number of empty trips andtravel time of each such trip. The frequency of empty trip was based on totalnumber of loads delivered at or picked up form each station rather than netflows. Further, instead of minimizing empty travel time, Malmborg’s schememaximized it. It meant that each vehicle, after being unloaded at a delivery


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station, was routed to the farthest station. The total empty vehicle travel timethus obtained was considered by Malmborg (1990) as an upper bound solutionas opposed to the lower bound in the Maxwell’s model. Further, Malmborg(1990) argued that actual empty travel would be a weighted average of thesetwo bounds. The weighing factor is a function of vehicle dispatching strategy.

3.3.2 Scheduling and routing algorithms for AGVs

Scheduling of AGVs aims to dispatch a set of AGVs to pick up the jobs from acentralized load/unload station and dispatch them to different workstationsaccording to some priority. Certain goals are kept in mind while deciding thescheduling of AGVs. These goals are related to resources such as minimizingthe number of vehicles while maintaining the system throughput or mini-mizing the total travel time of all vehicles (Akturk and Yilmaz 1996). Routingof AGVs involves finding a suitable route, e.g., shortest distance path, shortesttime path or minimal energy path for every vehicle from its origin to itsdestination based on current situation (Daniel 1988). For solving schedulingand routing problems of AGVs, many algorithms have been suggested in theliterature, e.g., Egbelu and Tanchoco (1986), Kolen et al. (1987), Bartholdiand Platzman (1989), Kaspi and Tanchoco (1990), Kim and Tanchoco (1991),Tanchoco and Sinreich (1992), Lin and Dgen (1994), Langevin et al. (1994,1996), Taghaboni and Tanchoco (1995), Akturk and Yilmaz (1996), Klien andKim (1996), Lee et al. (1996), Rajotia et al. (1998), Barad and Sinriech (1998)and Qiu and Hsu (2001). Qiu et al. (2002) conducted a detailed survey of suchalgorithms and suggested that the existing work can be classified into fourgeneral categories:

• Algorithms for general path topology• Path layout optimization• Algorithms for specific path topologies• Dedicated scheduling algorithms.

Works in the first category usually treat the problem as a graph theoryproblem and use approaches such as shortest path algorithm to get an optimalroute. Optimization techniques such as integer programming, have been usedfor path network design in which the routing control is generally simple.Algorithms for path networks are restricted to specific topologies such assingle-loop, multi-loops, meshes, etc., and these are developed to decide theroute and control of AGVs. Contributions made by different authors in thedevelopment of AGV scheduling algorithms have been discussed by Qiu et al.(2002).

3.3.3 Discussion on research related to FMS material handling

Several approaches have been applied to solve the design, routing andscheduling problems of AGVs. These approaches range from simple logic to

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typical mathematical techniques such as integer programming formulations,branch and bound techniques, heuristic and simulation. But the approachesdiscussed in the literature are quite diverse in nature and it is very difficult tofind out the optimal solution in real-life cases.

Hoff and Sarker (1998) also noted this difficulty and commented, ‘‘theAGV system is still a topic for research, as guide path, pickup and deliverystation location, idle position and dispatching rules for the vehicles are beingdeveloped and improved. The proposed algorithms and heuristics are allrefined and some are more computationally difficult than others.’’ Besidesthese difficulties in the use of proper techniques in preparation of differentalgorithms, following shortcomings may be noted in the reported researchwork:

1. Most of the analytical models, which have been developed for AGVsystem, deal with small number of AGVs and workstations. As thenumber of AGVs increase, they present non-optimal results.

2. Most papers concerning the design of AGV system ignore or simplify theempty vehicle traffic.

3. Very few authors consider the multiple design problems of AGV system.4. Mutual relationship between AGVs and other material handling devices

like AS/RS has not been adequately explored.5. Generally, the layout problems and control problems of AGV system

have been treated separately despite the fact that these issues are highlyinter-related.

A large number of techniques have been suggested and developed fordesigning and control of AGVs in different situations by various researchers.However, if these techniques are carefully examined then it is observed thatmost of these methodologies have been proposed by academic institutions orresearch laboratories and their actual feasibility in an industrial setting is quitelimited. Further research is still needed to find the industrial feasibility of suchtechniques and following questions are to be answered:

• What is the cost of AGV system in comparison to human labor in thecontext of FMS?

• What is the flexibility of AGV system in comparison to human controlledequipments?

• What is the feasibility of implementation of AGV system in different workenvironments?

• What is the flexibility range of AGV system and how can it handledifferent varieties of parts?

• What type of industry-based optimization techniques is available for AGVsystem path layout?

• What type of industry-based models is available for calculating the numberof vehicles for different work situations, dispatching rules, etc.?


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Countries like India, where labor is very cheap, hesitate to adopt theseAGVs because of their costly and complex controlling techniques. Productionmanagers generally argue that when human labor is cheaply and convenientlyavailable then, why to go for such complicated material handling systems.Human labor is highly flexible and does not require special control algorithms.In such an environment, automated material handling systems, present a bigbarrier in the implementation of a complete FMS.

3.4 Issue regarding flexibility and its measurement

Flexibility is one of the critical dimensions in enhancing the competitivenessof organizations. Manufacturers of discrete parts face increasing demands forsmall-to-medium sized lots of customized products, requiring a productionprocess, which can provide flexibility as well as economy (Kouvelis 1991).Technological developments and market demands are forcing organizations tobecome more flexible. As a result, process flexibility is fast becoming a majorpriority for many organizations. Flexibility is one of the most sought-afterproperties in modern manufacturing systems (Shewchuk and Moodie 1998).

A great deal of research has already taken place in defining various types offlexibilities in manufacturing environment. Different authors have definedflexibility in their own ways. According to Chen and Chung (1996), flexibilityrefers to the ability of the manufacturing system to respond quickly to changesin part demand and part mix. Das (1996) has defined it as the ability of asystem or facility to adjust to changes in its internal or external environment.Tsubone and Horikawa (1999) have suggested that flexibility can be defined asthe ability of a system to adapt quickly to any changes in relevant areas such asproduct, process and workload or machine failure. Several researchers haveclassified flexibility under different categories. Buzacott (1982) has classified itin two categories, i.e., job flexibility and machine flexibility. Park and Son(1988) and Son and Park (1990) have identified four types of flexibil-ity—process, product, demand and equipment flexibility. Browne et al. (1984)have proposed eight types of flexibilities including machine flexibility, routingand expansion, etc., Azzone and Bertele’s (1989) have suggested six types offlexibility: process, product, production, routing, expansion and volume flex-ibility. Sethi and Sethi (1990) have identified eleven types of flexibility:product, process, program, production, volume, routing, expansion, operation,machine, material handling and market flexibility. The identified types offlexibility typically refer to different elements and attributes of a productionfacility such as machine, product, processing, operation, routing, capacity,expansion, design and system (Das 1996). Other researchers have studied therelationship between flexibility and productivity (Gustavsson 1984; Buzacottand Madelbaum 1985).

While it is helpful to understand the fundamental concept of flexibility, it isalso essential to develop measurement systems for flexibility so that firms canbetter benefit from utilizing these concepts of flexibility. A flexibility measureis a formula, algorithm, methodology or the like, for generating a value for a

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given flexibility type under given condition (Shewchuk and Moodie 1998).Chen and Chung (1996) have suggested that without proper development offlexibility measurements and consequently, a better understanding of therelationship among different flexibility levels and system performance, it canbe quite difficult for firms to position manufacturing as one of their compet-itive priorities. They have discussed alternative measures for the assessment ofmachine flexibility and routing flexibility and have illustrated the capabilityand applicability of these measures with examples. Dixon (1992) has appliedfactor analysis technique for measuring three dimensions of flexibility, i.e.,mix, new product and modification. Das (1996) has provided a theoreticalbasis for measuring the flexibility of manufacturing systems. He has intro-duced a new concept of multiple levels of measures for each type of flexibility.Tsubone and Horikawa (1999) have compared the machine flexibility androuting flexibility in terms of manufacturing performance in different shopenvironments. They conducted a simulation-based investigation to analyze theimpact of these types of flexibility on the average flow time of parts undervarious conditions of job flow pattern. Hutchinson and Sinha (1989) have useda decision theoretic approach in developing a model to assist a decision makerin evaluating the cost-flexibility tradeoff. They have also examined the sen-sitivity of the model with respect to several parameters of interest. They havetried to quantify the flexibility in monetary terms. Similar attempts have beenmade by other researchers also like Son and Park (1987), Son (1991), Suresh(1990), Azzone and Bertele (1989), Troxle and Blank (1989), Venk (1990),Zahir (1991), Stam and Kuula (1991), Suresh (1991) and Demmel and Askin(1992).

3.4.1 Discussion on research related to FMS flexibility

From the above discussion, it is clear that a lot of efforts have been put bydifferent authors in defining and measuring flexibility. But in spite of this,following problems are generally faced in industries. First problem is with thedefinition of flexibility. Though many definitions have been used in the lit-erature, but there is no agreement on one general definition of flexibility. Thiscreates a lot of confusion in the minds of production managers in real-lifesituations. As Shewchuk and Moodie (1998) have pointed out, ‘‘there is nogeneral agreement on how to define flexibility. This is due to multidimensionalnature of flexibility and various views of flexibility that result: flexibility hasbeen viewed and studied as a physical property, an attribute of decision making,an economic indicator and a strategic tool.’’ Their viewpoint is strongly sup-ported by Beskese et al. (2004), ‘‘despite the wide interest, flexibility remains tobe poorly understood in theory and poorly utilized in practice. One of thereasons for this is the lack of general agreement on how to define flexibility.’’

The second problem in the domain of flexibility issue is its measurementand quantification. No clear-cut methodology is available to practicing man-agers for measuring the flexibility of a system. Classification of flexibility intovarious types has further complicated this situation. For example, product


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flexibility, operation flexibility, process flexibility, machine flexibility, pro-duction flexibility, manufacturing flexibility are terms, which look alike andare very difficult to be differentiated in practical industrial environment.These terms have only helped in enhancing the confusion in the minds ofmanufacturing managers and no other big objectives have been fulfilled withthese confusing terms. Owing to its nature, any measure of flexibility has to beuser or situation specific.

Several approaches, e.g., multi-dimensional approach, decision-theoreticapproach, Petri-nets approach and information theoretic approaches, etc.,have been suggested in the literature. But, their use in real-life cases is yet to beexplored. Most of the literature only presents a list of proposed measures butvery rarely mentions the objectives and criteria against which a particularmeasure can be judged. This explains the existence of many measurementschemes according to different types of flexibilities. Researchers have not beenable to develop and present the universally accepted techniques over which themanufacturing people can rely. This poor understanding about manufacturingflexibility is, thus, inhibiting the progress towards the utilization of flexibilityconcepts in industry and impeding manufacturing managers from evaluatingand changing the flexibility of their operations. Other shortcomings observedin relation to flexibility and its measurements are as follows:

• It is difficult to understand the classification or dimensions of flexibilityfrom the literature. It has been realized that there is still a large gapbetween the benefits promised by financial evaluation tools and the ben-efits realized in real life cases.

• It is difficult to measure the manufacturing flexibility. Practically, manag-ers in manufacturing industries face difficulty in calculation of machine androuting flexibilities in the absence of a clear-cut model for the same.

• Another problem in designing of FMS is related to how to reach to acompromise between flexibility and throughput of the system.

• Whenever discussion about transition to FMS takes place, the industrialmanagers put-up such type of blunt questions as (i) what is flexibility of themanufacturing system, (ii) is there any exact definition and nature of it,(iii) how exactly, can it be measured, (iv) is there any universally acceptedformula for its quantification, and (v) how can it be implemented?

• There are certain constraints regarding different resources in FMS whichinhibit to achieve the desired level of flexibility in the system, e.g., (i)machine tools have limited range to support a limited category of parts, (ii)robots are designed or selected for a definite range of parts, (iii) AGVShave well defined load carrying capacity, and they can be dispatched androuted through a fixed path and (iv) fixtures and other work-holdingdevices are highly rigid.

In addition to these, constraints regarding control methodology also actas the main barrier in making the production system a really flexible one.Production managers often feel handicapped in differentiating process

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flexibility and product flexibility. In such an environment, there is an utmostneed to develop models for the measurement and quantification of flexi-bility based on simple inputs such as variety of parts and availability ofdifferent resources.

3.5 Issue regarding machine tools in FMS

Machine tools are the main component of flexible manufacturing systems.Various types of machine tools like CNC machines and machining heads inSPMs, etc., are used in FMS. It has been reported in the literature that CNCmachines are the basic component of FMS because of their softwired nature.But, Overmars and Toncich (1994) have pointed out some limitations ofCNCs (e.g., longer cycle time; longer chip-to-chip tool change time; closedarchitecture of CNC machines, etc.) in the integrated FMS environment.Besides these drawbacks, associated with the usage of CNC machines, majorfactor is their high cost.

Another type of machine tools used in FMS is special purpose machines(SPMs). These machines are designed and selected according to somespecific product requirements, but whenever some design changes areconfigured in the products, then, such machines are not found to be prac-tically enough flexible to cope with the big changes in product design. Thissituation puts up a big barrier in the implementation of FMS. Hence, thereis a need to make these SPMs sufficiently flexible. One way to do so is tointroduce reconfigurability in the machining heads and use of CNCmachining heads instead of conventional machining heads. In this way,conventional SPMs would be called flexi-SPMs offering the desired flexi-bility in the manufacturing system.

Cutting tools and various issues related to them also affect the perfor-mance of an FMS. Cutting tools of different materials behave in differentways and they have different tool lives. For example, cemented carbidetools can be utilized at higher cutting speeds than high-speed steels whereas ceramics and CBN tools can be utilized at still higher cutting speeds thancemented carbides. Tool life of these tools is also different. When thesetools are worn out, they are sent to grinding section for resharpening.Grinding section may or may not be free at that time to serve them. Thesecutting tools may have to be in queue for getting resharpened. So, thisfactor puts a great constraint on the decision of loading and schedulingproblems. Gray et al. (1993) and Veeramaani et al. (1992) give extensivesurveys on the tool management issues of automated manufacturing systemsand emphasize that the lack of tooling consideration has resulted in thepoor performance of these systems. Kouvelis (1991) has identified cuttingtool utilization as an important parameter for the overall system perfor-mance.

One should also consider the nature and number of pallets and fixtures formounting the work-pieces on them. Whenever a new product is added, theproduction facility experiences a cost for setting up new cutting tools, fixtures,


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reprogramming of CNC machines, reconfiguring the machining heads in RMS(reconfigurable manufacturing systems) and down time cost of the productionfacility for new setup (Das 1996). Minimum set-up cost and time are the mainexpectations from FMS. But, practically following limitations or constraintsare generally experienced on shop floor conditions:

• New cutting tools (for new parts) may not be readily available.• FMS can produce parts that are large in variety and small batches. How-

ever, with the increase in part variety, the number of tool types and toolcost will also increase. Tool cost contributes to about 25%–30% of thetotal fixed cost and variable cost in a FMS (Keung et al. 2003).

• Fixtures represent the interface between the FMS and the parts to bemachined. But, generally, parts differ in shape, size, material and nature ofrequired operations and the fixtures may not accommodate this variety ofparts.

• There may be limits to the reconfiguration of machining heads for handlingthe new products in special purpose machines (SPMs).

• Set-up time for the new part may be high because of various constraintsrelated to cutting tools, machine tools and material handling devices. Thisputs a question mark on the flexibility to be achieved through FMS in realsense.

• There may be difficulty in reprogramming of the system.• Another problem is to change the layout of machines within the manu-

facturing system.• Atmani and Lashkari (1998) have rightly pointed out that though in

recent years, the concept of FMS has emerged as a viable answer to theproblems of flexibility and efficiency, but proper planning is a keycondition to FMS success in dealing with problems of poor utilization ofequipments and tools, and costly and frequent setups. Thus, the difficulttask facing the production planner is the optimal selection of machinesand tools.

• Tool magazine or tool turret capacity also influences the total number oftools to be used in the machining process. This puts constraints on the setof operations that can be assigned to a machine during a given period.Machine tool builders normally try to equip their machining centers withlarge tool magazines (e.g., 50–60 slots) in order to reduce the capacityconstraint. However, large tool magazines result in high seek times andsometimes greater than the time required to perform an operation (Griecoet al. 2001).

3.6 Issue regarding operation, control and maintenance techniquesfor FMS

Although flexible manufacturing systems have increased the flexibility andprovided system users with many other advantages but many companies have

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failed to take full benefits of these advantages because of increasing com-plexity in operation and control techniques. An FMS consists of a group ofmachines or other automated workstations, which form into modular sub-systems, such as CNC machines, robots, vision system and a process station.These are interconnected by a material handling system and usually driven bya computer (Maleki 1991). Individual controller unit controls each modularsystem and these controllers perform their intended tasks under the supervi-sion of a higher-level controller. Though many new techniques and technol-ogies such as neural networks, fuzzy logic, expert systems and artificialintelligence, etc., have been proposed in the literature for controlling andintegrating various components of FMS but majority of these controls andsimulation methodologies are highly complex. They are found to be untenablein practical FMS implementations and in a large context; it is difficult todetermine the industrial feasibility of such concepts and methodologies.Overmars and Toncich (1994) have pointed out that the interfacing betweenFMS controller and the individual group controllers (e.g., coordination ofgantry robot with CNC machines and other robots in order to avoid collisionor interference) is often complex, weak and unreliable where multi compo-nent manufacturing is concerned.

At a technological level, the main questions are concerned in getting dif-ferent hardware and software systems to work together. Most of the firms feelthat software represents the major technical problem. Hardware problemsmainly revolve around linking machine tools with handling systems and sincethe technological developments have been evolving incrementally over a longperiod, they pose few insurmountable difficulties. On the software side thepicture is more fluid due to rapid proliferation of operating systems, com-munications options and control programs. The key issue is one of softwareintegration—of providing structures and standards within which communica-tion can take place on and between different levels in a control hierarchy.

The key aspect of FMS is its ability to adapt to changes in the control tasks.This flexibility includes the quantities and varieties of part types which it canproduce, the orders in which the operations may be performed and the abilityto re-route parts back into flow paths. In short, the control platform shouldhave the capability to automate the flow of information (Mcdermott and Yao1997). Grieco et al. (2001) have found following drawbacks in the traditionalsupervisory control system:

1. It is difficult to integrate hardware devices supplied by different compa-nies into the same system.

2. It is difficult to integrate the FMS with other systems operating in thesame company.

3. It is difficult to integrate new sensors in existing control architecture.

These are not the only operation and control techniques, which createproblems in running of FMS, but maintenance of FMS is also of very complexnature. Vineyard et al. (1999) have reported that during an FMS’s extended


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useful life it will experience a different wear and tear history than a traditionalmachine tool operating during the same time period. According to them, anFMS will operate upto 90% utilization rate whereas a traditional machine toolwould probably be utilized at only 20% and it will result in the FMS incurringfour times the wear during any given time period. Hence, it will significantlyincrease the importance of maintenance. A typical FMS will not only havemechanical, but also electronic, hydraulic, electro-mechanical, software andhuman element, each having different failure rate distribution. In this respect,maintenance policies for FMS must focus on the understanding of the entiresystem instead of individual units or components because of integrated natureof units (Cho and Parlar 1991). Pintelon et al. (1995) have also noted thatcomplexity and stochastic nature of maintenance requirements in such envi-ronments make deciding on an appropriate maintenance policy a very difficulttask.

3.7 Issue regarding human element and culture in FMS

Even if FMSs have a high degree of automation, people are normally presentto supervise the system, load/unload parts, introduce new fixtures and removeunnecessary ones and remove worn tools and replace them with new ones.The behavior of the system is, therefore, affected by the availability of humans(Grieco et al. 2001).

Replacing a conventional manufacturing system with FMS alters thephysical work environment. No doubt, FMS results in improved work envi-ronment, however, the human impact of this transition has yet to be thor-oughly evaluated. Although the available literature includes a number ofethnographic studies and experimental work still there is a general lack ofrigorous methodology. Contradictory findings also indicate a real need foradditional research work in this area. Investigations focusing on the behav-ioral impact of FMS systems with respect to operators should be carried out.Since, implementation of full FMS means unmanned operations in the man-ufacturing area, so, there is very little need of human labor. Hence, existingwork force will not agree for such a transition and may put up a great resis-tance against the implementation of FMS. In particular, worthwhile researchis required for analyzing the impact of FMS on workers and the whole societyand should be able to answer the following questions:

• How many workers will be retrenched with the transition to FMS?• Can’t these workers be adjusted in other work areas?• What will be the training and relocation cost for such workers?• Whether the workers/employees support the idea of transition to FMS or

not? If not, then, how much resistance is expected from them?• Whether the organization is ready for any untoward consequences in this

age of competition?• Whether the organization can bear the loss of short-term or long-term shut

down?

26 T. Raj et al.

123

• What will be long-term human consequences of FMS implementation?• What types of additional skills and qualities are essential for FMS super-

visor?

Investigation in above mention areas can help clarify the relationshipbetween FMS performance and human element. All these studies may helpdetermine whether the FMS has positive, negative or very minute humanconsequences. In-depth case studies and experimental investigations areneeded to answer these important questions.

4 Identification of barriers

On the basis of the literature review and experiences of interaction withmanufacturing managers and academicians, it has been found that despite of alot of research in the area of FMS, there are certain barriers, which inhibit theproduction managers during transition to FMS. The purpose of this section isto identify these barriers.

Several authors have identified the high cost of FMS as a major reason forits low level of acceptance (e.g., Rao and Deshmukh 1994; Koren et al. 1999;Kumar et al. 2006). Rao and Deshmukh (1994) have noted that the adoptionof FMS involves a huge investment and a high degree of uncertainty. Kumaret al. (2006) have reported that as FMSs are very expensive, it is essential tomanage them effectively to achieve desired goals with less investment-risks.According to Koren et al. (1999), FMSs consist of expensive, general-purposecomputer numerically controlled (CNC) machines and other programmableautomation. It is not only the use of CNC machines, which adds cost to theFMS but other components, like robots, AGVS, automated storage andretrieval system, conveyor system and their control methodologies alsoincrease the cost of system extensively. Increased level of automation in FMSis also a matter of concern. No doubt, automation influences the efficiency ofsystem but at the same time, it should be cost effective. Efficiency of con-ventional machine tools is between 15% and 30%, with NC machine tools it isabout 50% while with machining centers it is about 75% and with FMS it is upto 90% (Balic and Pahole 2003). The efficiency of FMS is very high, but if wetake the economical aspects and cost effectiveness into account, the totalefficiency is low (Balic et al. 1995).

Another barrier cited by Nelson (1986) is uncertainty. The adaptation ofrelatively new technology, not surprisingly, increases the risk involved, owingto uncertainties such as those of implementation cost, implementation sche-dule and product performance. The adoption of ‘high-tech’ and expensiveproduction system such as FMS adds financial, technical and organizationalrisks to the firm (Gupta 1988). Ching and Loh (2003) have raised the issue ofgood management in successful implementation of FMS. Rao and Deshmukh(1994) have also concluded that implementing the high cost sophisticated


123

automated system is a general challenge for a manufacturing firm, as itrequires clear-cut goals and policy direction from top management.

Another barrier in transition to FMS is that of flexibility and its measure-ment. Craven and Slatter (1988) have discussed that most of the existing FMSsare not flexible in true sense of word. Although many systems do have volumeflexibility yet they lack in product flexibility, i.e., they can handle only alimited variety of different parts types. Many flexibility measurement schemesalso exist but there is a lack of universal acceptance of any one scheme.

Sharafali et al. (2004) have discussed that operating an FMS is morecomplicated than its conventional counterpart. Buyurgan et al. (2004) havealso noted that problems related to FMS are relatively complex as comparedto traditional manufacturing systems in which lead times are longer, inventorylevels are higher and utilization rates are lower. It becomes very difficult tosolve real-life FMS planning, scheduling and operational problems becauseeach machine in an FMS is quite versatile and capable of performing manydifferent operations, the system can machine several part types simultaneouslyand each part may have alternate routes through the system. Hutchison (1991)has reported that the design and operation of FMS has proven to be difficult inpractice due to their flexibility, complexity and need for system-wide coor-dination, which makes strategic and tactical decisions, complicated. Spur et al.(1986) have reported that the complexity of these systems has impaired theirreliability. Mak et al. (1999) have discussed that the design of FMS is a for-midable task and requires the expertise in various disciplines due to its highdegree of integration and complexity. One of the problems encountered in thedesign and implementation of FMS is the layout of machines within themanufacturing cells (Solimanpur et al. 2005).

Kouvelis (1991) and Gamila and Motavalli (2003) have discussed theproblem of tool management in FMS. Since a large number of tools are usedin FMS to machine a variety of parts, it requires a great degree of sophisti-cation to plan, control and monitor tools in the plant. A major bottleneck in atrue FMS is the inability of hard-dedicated fixtures to be setup and changedquickly and automatically (Markus et al. 1990). Set-up time for new parts istoo high. Set-up cost and maintenance cost are very high in an FMS(Buzzacott and Yao 1986).

Chan et al. (2000) raised the difficulty in design of FMS and commentedthat FMS design is a very complex task due to two important characteris-tics—(i) a wide variety of alternate system control strategies and configura-tions available to designers, and (ii) a task in which a variety of selectioncriteria are involved which are difficult to quantify. They, further, pointed outthat the current modeling techniques are not able to solve the FMS designproblem as a whole. According to Chan and Chan (2004), ‘‘there are manyoperational problems that arise in FMSs domain, for example, scheduling,routing and dispatching problems.’’ McCutcheon (1993) has also reported inhis work that many FMS projects are prone to severe implementation prob-lems because of their technical risks. He has listed the following factors, whichcontribute to technical risks of FMS:

28 T. Raj et al.

123

1. The system complexity.2. The amount of new hardware and software, which the system incorpo-

rates.3. The number of new kills and routines required for its operations, and4. The builder’s experience with the technologies involved.

Based on the literature review and discussions with the experts both fromindustry and academia, 23 barriers were identified. These barriers are enlistedin Table 1.

These barriers are listed in a generalized manner. It is not necessary that allthe organizations, which are opting for transition to FMS or implementationof it, will face all of these barriers. But the type of barriers and their impactdepends upon the type of organization, type of its end products and theirnumber of models, etc. Hence, any organization opting for FMS must foreseethe types of barriers and their impact. They must evaluate the feasibility oftransition to FMS before its adaptation.

5 Summary and conclusions

The confusion starts from the reported definition of FMS itself in the litera-ture. There is a lack of general agreement over just what an FMS is. Defini-tions of FMS, provided by different researchers, vary in nature. Some authorsdefine FMS as a system consisting of CNC machines linked by automaticmaterial handling systems while others include inspection equipments likecoordinate measuring machines or machine vision and/or even washing sta-tions in their definition of FMS. Some specify AGVs for parts loading andunloading while others specify robots for the same purpose. This creates a lotof confusion in the minds of industry people as to how many and what type ofcomponents should be included in an FMS.

Not only its definition, but also other issues related to FMS design andplanning such as loading, scheduling, material handling, etc., have not beenaddressed in a practical way in the literature. Most of the researches have beenreported by academia as theory-based approaches, which are difficult to bedigested by industry experts. Many technical difficulties associated with FMSimplementation are felt in real-life scenario and these technical problems haveslowed down the growth rate of FMS adaptation. Some surveyors have alsoreported failure results of FMS. This is the main reason that in the initial phasesof FMS adoption, for example during 1980s, though adoption of FMS becamethe central objective in the push for progress for many manufacturers butslowly its lustre started to fade and situation became more fluid because ofmany technical problems. Practicing managers have found it difficult to exploitthese multi-machine systems to their full potential. Today, the situation is soworst that some manufacturing experts are not interested, even, to discuss thishigh technology system, which was, once, acclaimed as the ultimate weapon inboosting productivity and competitiveness for the industries.


123

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30 T. Raj et al.

123

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05)


123

It is very much true that FMS has many advantages and a number of successstories about its utilization have been reported in the literature but, franklyspeaking, it is not easy to ride this wild horse. Every thing regarding itsimplementation as well as its use is not as smooth as has been reported inmany research articles. But, behind the scene, the story is quite different andinteresting to explore. The warning issued by Green (1986), today also, carriessufficient weight: ‘‘Don’t be misled by what appear to be successful installationstories...Remember that for the benefits gained each user has most likely gonethrough an extensive shakedown. Most FMS stories don’t tell you about thebirth pains, they just brag about the baby.’’ (Source: O’Grady 1989).

It is agreed that though good research work has been done in the domain ofFMS but, until unless, it is not handy for its easy and smooth implementationin real-life cases, then, what is its use? But, every thing is not lost and there is,still, a ray of hope. Only need is to pay proper attention towards the devel-opment of such techniques, which make the FMS a successful one. In order tohave a true FMS, all its components, e.g., work-holding and work-transferringdevices like fixtures and pallets, AGVs and robots, machining heads, etc., areto be flexible.

If any industry decides to shift to FMS, then, it becomes important to knowthe feasibility of transition to FMS. Not only this, one should also know thesituations in which it is preferable to other manufacturing systems. Thecompany should know the potential benefits from FMS before a detailedanalysis is carried out. Adaptation of FMS is not only a technical issue but alsodependent upon other features such as type of products and their volume,readiness on part of the management, etc.

It is generally found that the best way of judging the feasibility of FMS is togather knowledge about its industrial feasibility. More knowledge about itssystem components means that one can better understand its design andoperational concepts and have deeper insight into crucial variables during itsimplementation. For this purpose, answers to the following questions can besearched:

1. What types of concerns do motivate the company to adopt or transit toFMS?

2. How much increase in flexibility, productivity, automation and economicadvantage is anticipated with the transition to FMS?

3. How many FMS applications are there in the industry and at what ratethey are being installed? What is their success/failure rate?

4. To what extent, the transition to FMS is applicable? Is a total plantconversion to FMS a feasible and desirable option?

5. What will be the cost of this changeover?6. What will be the payback period?

For finding the answers to these questions, literature reviews, mail, andtelephonic surveys, case studies may be conducted. In case studies, organi-zations that are currently using FMS, can be studied in detail and the findings

32 T. Raj et al.

123

may be documented in the form of comparisons of firms and their systemcharacteristics.

It is true that questions and gaps, identified in relation to different issues,are really irritating and deserve proper research attention. Both academia andpracticing manufacturing managers should discuss all these issues on a com-mon platform and future research should be oriented towards addressing coreissues in the feasible implementation of FMS in real-life situations. At themoment, we conclude our work with the following comment in response tothat of Green (1986): ‘‘No doubt, baby’s birth is associated with different kindsof normal pains, even caesarean may be necessary in some cases; if the baby ishale and hearty, then, all such kinds of pains are forgotten. Only need of thehour is to design and develop different kinds of painkillers in the form ofhardware and software technologies which make the FMS implementationeasier.’’

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Mr. Tilak Raj is the Assistant Professor in MechanicalEngineering Department in YMCA Institute of Engineering,Faridabad, India. He is B.Sc. (Engg.) in Mechanical Engg.,M.E. in Production Engg. and pursuing his Ph.D. research.He has consulted Indian industry on improvement in themanufacturing techniques. His area of expertise is manu-facturing technology.

Dr. Ravi Shankar is the Associate Professor in theDepartment of Management Studies, Indian Institute ofTechnology, Delhi, India. He is the Group Chair of SectoralManagement and Programme Coordinator of MBA (Tele-com) at IIT Delhi. His areas of interest are OperationsManagement, Supply Chain Management, KnowledgeManagement, Operations Research, and Telecom SystemsManagement. He is Ph.D. from IIT Delhi. His research pa-pers have appeared in IEEE Transaction on Systems Manand Cybernetics Part A: Systems and Humans, EuropeanJournal of Operational Research, International Journal ofComputer Integrated Manufacturing, OMEGA: The Inter-national Journal of Management Science, Decision SupportSystems, International Journal of Production Research, In-ternational Journal of Production Economics, International

Journal of Supply Chain Management, Computer and Operations Research, Computer and Ind.Engg., International Journal of Quality & Reliability Management, etc.

Dr. Mohammed Suhaib is the Associate Professor inthe Department of Mechanical Engineering, Faculty ofEngineering & Technology, Jamia Millia Islamia (A CentralUniversity), New Delhi, India. He is B.Tech., M.Tech. andPh.D in Mechanical Engineering. His areas of research interestare Robotics, Automation & Manufacturing, and FlexibleManufacturing Systems.

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