EDITORIAL - Technical University of Valencia

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International Journal of Production Management and Engineering

Volume 3, Issue 2June - December 2015

Pages 87-133

EDITORIAL

EDITORIAL

EdItors

Prof. Eduardo Vicéns-Salort, Universitat Politècnica de València, Spain

Dr. Pedro Gómez-Gasquet, Universitat Politècnica de València, Spain

Dr. Andrés Boza, Universitat Politècnica de València, Spain

Dr. Josefa Mula Bru, Universitat Politècnica de València, Spain

Dr. Llanos Cuenca, Universitat Politècnica de València, Spain

EdItorIal Board

Prof. Luis M. Camarinha-Matos, New University of Lisbon, Portugal

Prof. Ramón Companys Pascual, Universitat Politècnica de Catalunya, Spain

Prof. Luiz C. R. Carpinetti, University of Sao Paulo, Brazil

Prof. Bernard Grabot, University of Toulouse, France

Dr. Susan Grant, Brunel University, United Kingdom

Prof. Paul W. P. J. Grefen, Eindhoven University of Technology, Netherlands

Prof. Roland Jochem, Fraunhofer - Institute for Production Systems and Design Technology, Germany

Prof. Francisco C. Lario Esteban, Universitat Politècnica de València, Spain

Prof. Andrew C.L. Lyons, University of Liverpool Management School, United Kingdom

Prof. Luis Onieva Giménez, Universidad de Sevilla, Spain

Prof. Angel Ortiz Bas, Universitat Politècnica de València, Spain

Prof. Raúl Poler, Research Centre on Production Management and Engineering Universitat Politecnica de Valencia, Spain

Prof. José Carlos Prado-Prado, Universidad de Vigo, Spain

Prof. François Vernadat, European Court of Auditors, France

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ISSN 2340-5317EISSN 2340-4876Depósito Legal V-1737-2013

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International Journal of Production Management and EngineeringPME

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table of contents

PaPErs

total Quality Management implementation in Greek businesses: Comparative assessment 2009-2013 ................ 87Vranaki, M., Vranakis, S. and Sarigiannidis, L.

Quantitative assessment of sustainable city logistics .............................................................................................. 97Grosso-delaVega, R. and Muñuzuri, J.

Fuzzy maintenance costs of a wind turbine pitch control device ........................................................................... 103Carvalho, M., Nunes, E. and Telhada, J.

Which of dEa or aHP can best be employed to measure efficiency of projects? ................................................... 111Sánchez, M.A.

a robust evaluation of sustainability initiatives with analytic network process (aNP) ........................................... 123Ocampo, L. and Ocampo, C.O.

iiiCreative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Int. J. Prod. Manag. Eng. (2015) 3(2)

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http://dx.doi.org/10.4995/ijpme.2015.3245

Received 2014-08-26 Accepted: 2015-05-25

Total Quality Management implementation in Greek businesses: Comparative assessment 2009-2013

Martha Vranakia,i, Stergios Vranakisa,ii and Lazaros Sarigiannidisb

a Democritus University of Thrace, School of Engineering, Department of Production and Management Engineering. Xanthi, Greece

a,i [email protected],i [email protected]

b Technological Educational Institute of Kavala, School of Business and Economics, Department of Business Administration. Kavala, Greece

b [email protected]

abstract: The competition in the Greek manufacturing sector has become very intense and the need for businesses to survive, under these very difficult conditions, forces them to find new ways to increase their profits, but also to attract new customers and to retain old. A necessary condition for long-term business survival is to maintain a high product quality level. The implementation of Total Quality Management (TQM) approach is a key factor to achieve this goal.The main objective of this research is to identify the current situation as far as the implementation of TQM by Greek manufacturing firms, and finally to compare the results between the current research and the previous research of 2009 (Vranaki et al., 2010). The research model that has been developed incorporates nine factors which are found in literature to influence Total Quality Management. A structured questionnaire has been developed and distributed to executives of 61 companies. Descriptive statistics as well as Structure Equation Modeling (SEM) techniques were used to analyze the data.

Key words: Quality, Total Quality Management, Business Performance, Management Leadership, Supplier Management, Customer Focus.

1. Introduction

In the first decades after the Second World War, the competitiveness of products and services in international trade was defined by two related features, quality and production cost. A more recent important dimension of competitiveness is the ability to develop innovations in products and production processes. The ability to develop innovations frequently combined with quality and productivity, determine the time our chances to survive a business in a complex and uncertain environment in a context of rapid globalization, technological developments.

Moreover, development in recent decades led many companies to consider the quality as the basic and most effective condition for success. This explains the ease of penetration in foreign countries many products in Japan and Germany, software packages and various technical and financial services in the U.S., and their example followed by South Korea,

Singapore, Taiwan etc. . What ultimately establish and differentiate the products of the countries is the high quality that offer the purchaser in relation to their cost, in other words a great value compared to cost to the customer.

2. Literature Review

In recent years, increasing attention has been paid to improving the overall quality. Many companies have taken initiatives to implement various techniques of quality management. An important strategy for achieving high quality is TQM (Total Quality Management). The Total Quality Management (TQM) was defined as a management system to improve efficiency within a business to maximize customer satisfaction, conduct continuous improvements and great support to the involvement of employees.

87Int. J. Prod. Manag. Eng. (2015) 3(2), 87-95Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International



2.1. Factors that affect operational resultsImproving business results through quality, assumes that many factors are quite important for enterprises. A comprehensive review and a classification of the relevant empirical literature revealed that, in general, the nine factors discussed below were the most important parameters in the application of TQM.

2.2. LeadershipAs documented by various researchers (Deming, 1986; Juran, 1986), the administrative leadership is an important factor in the implementation of TQM because it improves performance by influencing other practices of TQM (Anderson et al., 1995; Flynn et al., 1995; Ahire and O’Shaughnessy, 1998; Wilson and Collier, 2000). The successful implementation of TQM requires effective change in the culture of a company. It is almost impossible to have changes in a company without any a concentrated effort by the administration, which aims at a continuous improvement in an open communication and cooperation throughout the enterprise (Bell and Burnham, 1989; Ettkin et al., 1990; Goodstein and Burke, 1991; Handfield and Ghosh, 1994; Choi, 1995; Hamlin et al., 1997; Zeitz et al., 1997; Daft, 1998; Abraham et al., 1999; Adebanjo and Kehoe, 1999; Ho et al., 1999).

2.3. Human resource managementThe administration has a complex role in the implementation of TQM. It is impossible to improve the procedures of any business without a well-trained workforce. The management of human resources, previously known as personnel management, has been upgraded to the science that studies the staff not as a factor that causes costs, but as an asset in which each company must invest. The administration should provide the necessary resources for the training of staff in the use of new concepts and tools and creates a work environment that encourages employee participation in the process of change (Bell and Burnham, 1989; Schroeder et al., 1989; Burack et al., 1994; Anderson et al., 1995; Flynn et al., 1995; Hamlin et al., 1997; Ahire and O’Shaughnessy, 1998; Daft, 1998; Handfield et al., 1998; Ho et al., 1999; Wilson and Collier, 2000). Top management should also ensure that the necessary resources for the relevant quality training is available (Ahire and O’Shaughnessy, 1998; Anderson et al., 1995; Flynn et al., 1995; Handfield et al., 1998; Ho et al., 1999). It takes more than education to be effective and successful change. Employees should be involved

at this stage. A crucial factor in accordance with the Adebanjo and Kehoe (1999), is that the participation of workers, because affected by the creation of a new working environment that encourages and facilitates open communication. In such an environment, it is possible for workers to commit themselves to work and contribute their ideas in that it facilitates and enhances the process of change (Burack et al., 1994; Anderson et al., 1995; Flynn et al., 1995; Das et al., 2000).

2.4. Information and data analysisThe information and analysis of data related to quality, including the unnecessary actions of a “poor” quality, such as repetitive labor costs, waste and control charts to identify quality problems and provide information on the areas of potential improvement (Choi, 1995; Lockamy, 1998; Ho et al., 1999). The data relating to quality have a positive effect on firm performance through three business practices of TQM. Specifically, through the quality management of suppliers, to design new products / services and through management processes.

2.5. Supplier managementSince all businesses (especially large) have their suppliers from whom they buy either materials or products, the quality that they provide them is able to affect the overall quality of the finished products. So the complete identification of products needed by their suppliers a company is a hub avoiding production of defective products and, therefore, increase business performance. The quality management of suppliers requires regular monitoring of suppliers by creating a database that measures this performance, a critical tool for improving material and raw materials costs required to develop, market prices and responsiveness of suppliers (Krause et al., 1998). With this database, companies can pursue qualitative measures such defective parts-per-million (parts-per-million defective), the reliability and the rate of discarded products (Forza and Flippini, 1998; Krause et al., 1998; Trent and Monczka, 1999), as well as timely delivery and performance in the percentage of acceptable incoming materials (Tan et al., 1998).

2.6. Product designEach product has specific characteristics. For the design of the process or production processes, products are categorized into groups depending on

88 Int. J. Prod. Manag. Eng. (2015) 3(1), 87-95 Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International

Vranaki, M., Vranakis, S. and Sarigiannidis, L.


their type, their production volume, complexity, and on the basis of characteristics of the contact point with these firm-customer. Regarding the model can distinguish the specific products that can be manufactured in many different styles, standard products and products of mass production (Adamidis, 2002). Under the IGI, efforts design new products have two objectives: planning the construction part of the products, and the design quality of the products (Flynn et al., 1995; Handfield et al., 1999).

Top management of a company is responsible for the design of products for the market and meet consumer needs (Deming, 1986; Garvin, 1987; Shetty, 1988; Flynn et al., 1995). This focus is critical for the de-velopment of products, especially when they meet customer needs (Juran, 1981; Leonard and Sasser, 1982; Flynn et al., 1995; Hackman and Wageman, 1995). To simplify the design of products, top man-agement uses interoperable groups to reduce the number of parts that make up the product and stan-dardize these parts (Chase et al., 2001). By doing so achieves a more efficient management processes by reducing process complexity and differences be-tween the procedures (Flynn et al., 1995; Ahire and Dreyfus, 2000).

2.7. Process managementAnother factor that affects the operational results through management procedures. Management processes in an enterprise implies a proactive method to improve the quality, such as the design of processes that provide stable production schedules and distribution work (Saraph et al., 1989; Flynn et al., 1995) to reduce the complexity of processes (Flynn et al., 1995) with the build quality of the product during the production phase (Handfield et al., 1999). Reducing the complexity of the process increases the uniformity of production, while reducing duplication and defective (Anderson et al., 1994; Forza and Flippini, 1998) because the quality problems are identified and corrected immediately (Ahire and Dreyfus, 2000). The process used to produce a product directly affects the quality. The market, for example, a machine that will facilitate the production and thus improve the quality of a solution where the money will be invested in the market will be amortized from the best production, the easiest and best price sale.

2.8. Customer focusOne element of TQM is the focus on customers. The establishment and maintenance of an open

relationship between the firm and its clients facilitate the design of new products. This is achieved because there is immediate clarification of needs and wants of customers. The key to nurturing strong relationships with customers is to establish communication between the firm and its clients (Tillery, 1985). These practices include frequent contact with customers. The Wright and Snell (2002) argue that simply focus and customer acquisition is not always good for business. Since customers can easily be lost in case they have a bad experience with the product or even if a new product does not attract them. Businesses should target customer trust to have improved operational results.

2.9. Strategic PlanningStrategic planning is the process of development and analysis of the mission and the vision, objectives, strategies and defining the sources of business. Strategic planning has a long time horizon, considering the external environment and determines the general direction of the business. This programming will be made by the highest levels of administration (Jackson and Ferguson, 1952).

3. Proposed conceptual framework and research hypotheses

Through this research aims to study o Role of TQM in Greek businesses, and the comparison to the applications of the principles of TQM in the years 2009 and 2013. Research model is a synthesis of research findings from the literature. The opinions are varied and numerous, so an attempt was made to include as much as the model to be an integrated presentation that takes into account all factors affecting the IGI.

The 9 factors of TQM presented will serve as part of the model. All are interrelated and the proper functioning of one affects the proper functioning of the other. All are considered particularly important for an enterprise to improve its results, should take them seriously. Even the improvement of some of these factors will lead to greater earnings. Since the model will create some initial assumptions that depending on the findings of the investigation or will be verified or disproved. The research model is framed by an external agent is the economic crisis. The processing in our country now is now at breaking point. The economic crisis and the number of bureaucratic barriers that are in any healthy business initiative have created uncertainty and insecurity in the market.




Meanwhile, the business of the country, especially manufacturing companies based in the Greek region, seeking development measures that the state promised one part, and the other was obliged to take to remove disincentives to entrepreneurship and the establishment of structural economic reforms. Given the negative sentiment in the market, it is obvious that a new approach to the development effort, especially in the field of policies to improve the external business environment in Greece.

As shown in Figure 1 above, created the following assumptions:

- Hypothesis 1: The administrative leadership positively affects: a) strategic planning, b) customer focus, c) the information and data analysis, d) human resource management, e) management procedures, f) and supplier management.

- Hypothesis 2: The strategic planning positively affects: a) customer focus, and

b) operational results.

- Hypothesis 3: The focus of customer positively affects business results.

- Hypothesis 4: The information and data analysis positively affects: a) strategic planning, b) customer focus, c) the design of products, d) human resource management, e) management procedures, f) managing suppliers.

- Hypothesis 5: The management of human resources positively affects:

a) the management of suppliers, b) customer focus, and c) operational results.

- Hypothesis 6: The process management positively affects business results.

- Hypothesis 7: Managing suppliers is positive: a) designing products, and b) operational results.

Figure 1. Proposed Conceptual Framework.

Figure 1: Proposed Conceptual Framework

Human Resource Management

Strategic Planning

Customer Focus

Leadership

Supplier Management

Product Design

Business Results

Process Management

Data Analysis

H1a

H1b

H1c

H1d

H1e

H1f H2a

H2b

H3

H4a

H4b

H4c

H4d

H4e

H4f

H5a

H5b

H5c

H6

H8

H7a

H7b




- Hypothesis 8: The product design positively affects management procedures.

4. Research MethodologyField survey of the research are Greek companies that belong to the manufacturing sector of Greek economy, and employ more than 20 employees. The final sample consisted of 61 correctly completed questionnaires from the secondary sector. 67 of the 95 companies responded, returning 61 completed questionnaires. However, questionnaires six of them, were deemed unsuitable because responses completed poorly. Therefore 61 questionnaires (from 67 firms) were assessed as suitable for statistical analysis with a response rate of approximately 64.21% of the total population (95).

5. Exploratory Factor AnalysisOne measure of sample adequacy is the ratio of the Kaiser-Meyer-Olkin (KMO), and must take values greater than 0.5 (Malhotra, 1999). In this study, the KMO values are satisfactory and acceptable. An additional check of the correlations of our data is

testing sphericity of Bartlett (1950). Note that variables removed from the tables because of low loadings (see Appendix). The results of the checks carried out, allow to assert, that the deterministic variables are compact and reliable structures, able to contribute to the measurement of the agent to which they belong. To assess the goodness of fit of deterministic variables applied confirmatory Factor Analysis. Initially, took control of the overall model, and then testing the structural model.

In the model below, the encodings are as follows: A. Leadership, B. Strategic Planning, C. Customer Focus, D. Information & Data Analysis, E. Human Resource Management, F. Process Management, G. Supplier Management, H. Product Design, I. Business Results.

The overall model was estimated using four indicators. Acceptable values of the indicators are: CMIN/DF<3, GFI>0.9, CFI>0.9, RMR<0.05 (Smith & McMillan, 2001). The levels of these markers suitability is acceptable, so the model is valid.

In summary, it should be noted that at first glance, observed that the main core of the model is the administrative leadership and information and analysis. The first factor directly influences the

Figure 2. Fitness Model.

CMIN/DF CFI GFI RMR1,946 0,878 0,847 0,041

Figure 2: Fitness model

CMIN/DF CFI GFI RMR 1,946 ,878 ,847 ,041

Table 1: Results of hypothesis testing

Hypotheses Investigated relationships Regression Result 1a Α B 0.31*** Accepted 1b Α C - Rejected 1c Α D 0.60*** Accept 1d Α E 0.21*** Accept 1e Α F - Rejection 1f Α G - Rejection 2a Β C - Rejection 2b Β I - Rejection 3 C I - Rejection 4a D B 0.19*** Accept 4b D C 0.24*** Accept 4c D H - Rejection 4d D E 0.19*** Accept 4e D F 0.25*** Accept 4g D G - Rejection 5a E G - Rejection 5b E C 0.25*** Accept 5c E I - Rejection 6 F I - Rejection 7a G H 0.46*** Accept 7b H I 0.31*** Accept 8 I F - Rejection

***p<0.001 level, **p<0.05 level

,00

A

,15

B

,29

C

,36

D ,21

E

,40

F

G

,42

H

,58

I

e1 e2

e3

e4

e5

e6

e7

e8

e9 ,25

,60

,24

,19

,31

,25

,31

,31

,46 ,44

,46

,70

,23

,19

,00




strategic planning, the factor relating to information and data analysis and management of human resources, which verifies three of the initial assumptions. While indirectly affects the other actors and the operational results. The second factor is that the core is the information and data analysis, which directly-affected in human resource management, the operational results and customer focus.

6. ConclusionsThe aim of this study was to analyze the factors affecting the IGI operating results, the impact of the economic crisis and to compare the results of this research with the corresponding 2009. Comparing the results of this research with the research conducted in 2009 (Vranaki et al., 2010) resulted in the following conclusions:

1. To improve operational results, emphasis should be placed on all factors of TQM.

2. Focusing on customers is a key objective of Greek firms.

3. Changes in customer preferences significantly affect the management of suppliers.

4. Factor information is a “station” of administrative leadership.

The first and very impressive conclusion drawn from this research are the indirect effects that accept

business results, which verified in earlier research (Vranaki et al., 2010). It was expected that these factors will directly affect business performance to some extent. The significance of this finding is the indirect influence of these factors on business outcomes. The interpretation of the above can be a very useful tool in the hands of Greek firms. More specifically, from the above we understand that companies need to pay attention to many parameters to achieve their purpose. It is not enough to be consumed in a particular agent and others to fail.

The focus of the customer no effect. Unlike the earlier survey where the customer focus impacted upon four factors, and this in turn is impacted upon the management of suppliers. Thus, we conclude that the customer satisfaction and knowledge on the requirements of customers is the second most important goal you want to achieve the Greek companies, but also that most businesses do not make changes in supplier management with the slightest change in customer needs. The administrative leadership does not act directly to target customers, but indirectly through other factors, in contrast to the 2009 survey. Administrations business to achieve its approach and establishment of good relations with clients through the collection of information, training workers but also through product design. At this point it should be noted that research verifies the Wright and Snell (2002), The who argue that simply focus and customer acquisition is not always good for business.

Table 1. Results of hypothesis testing.

Hypotheses Investigated relationships Regression Result1a Α → B 0.31*** Accepted1b Α → C - Rejected1c Α → D 0.60*** Accept1d Α → E 0.21*** Accept1e Α → F - Rejection1f Α → G - Rejection2a Β → C - Rejection2b Β → I - Rejection3 C → I - Rejection4a D → B 0.19*** Accept4b D → C 0.24*** Accept4c D → H - Rejection4d D → E 0.19*** Accept4e D → F 0.25*** Accept4g D → G - Rejection5a E → G - Rejection5b E → C 0.25*** Accept5c E → I - Rejection6 F → I - Rejection7a G → H 0.46*** Accept7b H → I 0.31*** Accept8 I → F - Rejection

***p<0.001 level, **p<0.05 level




Since customers can easily be lost in case they have a bad experience with the product or even if a new product does not attract them. The Greek companies surveyed are showing great interest in customer retention. Besides, studies have shown that attracting new customers is much more expensive strategy than keeping existing customers (Kotler, 1982). The Greek firms, given the large and increasing competition are trying to focus on customer satisfaction rather than on improving operational results, but for obtaining “good reputation.”

The next conclusion we reached was that the administrative leadership through the management of human resources affects product design product design. In the 2009 survey design products in turn impacted upon on business outcomes. However, it is very encouraging that most companies place great emphasis on training their employees. Course, must be included with the necessary resources for the training of staff in total business expenses. On the other hand, when a company has fully trained staff on quality issues as avoiding possible mistakes and defective products and therefore achieves customer focus. It should be noted that the management of human resources including the health and safety of workers. As we can conclude, businesses protecting employees from any accidents aimed at improving their emoluments as well as to improve the image of the company. Furthermore, observed that the level of training of governing and management procedures, which is repeated from 2009. Process management involves reducing the complexity of the processes in the production stage. Officials, however, the companies to be able to respond to change a process must first have the proper training. In any other case,

the “change” in business processes will have no positive benefit to business results.

Vendors directly affect the design of new products. Any change in production processes or customer habits involves the review of suppliers. As mentioned above, the quality of raw materials of products is the basis for good quality of finished products.

Finally, reference should be made to study the eco-nomic crisis as an external factor. The economic cri-sis, according to the frequency analysis, seems to have the most negative effects on human resource manage-ment and management of suppliers. This was expect-ed, considering the increase in the unemployment rate in the country the last two years, but also the need for companies to increasingly seeking “best prices” for their raw materials.

6.1. Research limitationsObserving the results of the investigation, it is useful also to refer to some restrictions. The survey was conducted with a sample of 61 Greek and craft industries in the manufacturing sector. A larger sample would likely give different results.

All companies operate in manufacturing sector, but 45 of the 61 belong to the food industry, so they subject to each agent from a different perspective, than if it were operating in different manufacturing activity.

Questions contain elements of subjectivity. Thus, some of the respondents may be overestimated to a question by scoring 1 in Likert scale that can be “worth” 2 or underestimated some grading at 7 in the Likert scale that can ‘deserved’ 6.

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http://dx.doi.org/10.1002/(SICI)1520-6564(199924)9:1<1::AID-HFM1>3.0.CO;2-L

http://dx.doi.org/10.1016/S0272-6963(98)00030-8

http://dx.doi.org/10.1108/01443579810217440


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http://dx.doi.org/10.1016/S0272-6963(89)80002-6


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Appendix 1. Check the one-dimensional nature and reliability

Factors Variables Loading KMO TVE Bartlett’s Sig Cronbach alpha

Leadership

Aa1Aa2Aa3Aa4Ab1Ab2

0,7450,7100,6900,6680,7100,750

0,750 60,386 0,00 0,675

Strategic PlanningB2B3B4

0,7300,9070,809

0,584 67,025 0,00 0,747

Customer focus

C1C3C4C5

0,6710,5220,8140,692

0,625 52,623 0,00 0,596

Information and data analysis

Da1Da2Db2Db3Db4Db5

0,6770,7920,8770,8560,8500,614

0,748 62,575 0,00 0,705

Human resource management

Ε1Ε2Ε3Ε4

0,7340,8100,7270,513

0,685 51,647 0,00 0,658

Process managementF1F2F3

0,6740,7460,731

0,611 51,514 0,00 0,525

Supplier managementG1G3aG3b

0,7020,6500,673

0,801 52,070 0,00 0,674

Product design

H1H2H3H4

0,7800,7100,7520,561

0,664 50,846 0,00 0,664

Business results

I1I2I3I4I5

0,8260,7760,5400,6670,652

0,704 56,941 0,00 0,704



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Quantitative assessment of sustainable city logistics

Rafael Grosso-delaVegaa,i and Jesús Muñuzuria,ii

a Dpto. de Organización Industrial y Gestión de Empresas II. ETSI. Universidad de Sevilla. Camino de los Descubrimientos, s/n. Isla de la Cartuja, 41092, Seville, Spain

a,i [email protected],ii [email protected]

abstract: The aim of this paper is to seek an answer to an specific question: how to make city logistics sustainable? This question in principle has no specific answer. By contrast, it could be answered in many and varied ways. Behind the search for some of these answers lies the development of a roadmap which this work aims to present. The research lines, the theoretical framework and methodology of the roadmap will be explained. Although the current status of the roadmap, its duration and timing still need to be completed, the main facts, as well as the results obtained to date and the expected results are here presented.

Key words: City logistic, Sustainable policies, Access time windows, Waste collection, Optimization.

1. Introduction

1.1. The paradigmatic frameworkA multiplicity of different kinds of goods are constantly entering, transiting and leaving urban areas: consumer goods, building materials, waste, packaging and mailings, etc. (Dablanc, 2007). It is well known that the urban freight transport includes heterogeneous goods and different types of vehicles of different sizes. In addition, the movement urban goods has a direct and fundamental influence on economics (Muñuzuri et al., 2005) and is vital to industry performance. That is why urban freight management is a necessary challenge which nevertheless implies a high complexity. But goods are not only transported in urban environments, so the first problem which efficient management of urban freight transport finds is the very notion of transport itself and its variants (Figliozzi, 2012).

Traffic congestion has become a daily phenomenon due to the increasing amount of traffic and the limited capacity of the road network. And these growing delays are very costly for both private road users and logistics and distribution providers. This causes high economic costs to these providers, in an attempt to avoid possible delays in deliveries or

collections to customers, by additional vehicles and its own drivers. In addition, violations of driving and traffic rules need close attention. Furthermore, it increases externalities related to the environment, such as emissions of CO2.

Another important problem that urban freight transport has to face is the urban morphology. European cities have several common characteristics that influence directly their mobility and their businesses. Likewise it imposes some restrictions on the flow associated to the supply of goods. First, most of its city centers have a radial structure with a high concentration of shopping areas, restaurants and other centers of social attraction. This structure, which is inherited from the Middle Ages, generates asymmetric flows of people (going to work, to shop, to eat or to visit tourist attractions) with those flows associated with goods. Parking problems, which virtually exist at the center of all urban areas, increase in Europe due to its peculiar morphology consisting of alleys and narrow streets (Ligocki & Zonn, 1984; Muñuzuri et al., 2012b).

In addition, the road transport sector in Spain has not been considering City Logistics as an industrial subsector. Therefore, there are no databases showing the importance of this activity.




On the other hand, from the 90´s the concept of sustainable development has been attracting worldwide attention. Sustainable development has proven to be an enduring and compelling concept because it points towards a clear management policy. Also, it is also flexible enough to adapt to new challenges, technological and economic conditions and social aspirations. It appeals to the general public and the scientific community in particular, as it involves a systemic view of economy and ecology, and requires solutions that protect the interests of future generations (Goldman & Gorham, 2006).

Sustainable development meets the needs of the present without compromising the ability of future generations to meet their own needs. It is widely accepted that these needs include economic, social and environmental developments (see Figure 1).

This “triple” point of view understands that development should be bearable (socially and environmentally), fair (socially and economically) and viable (environmentally and economically) and, therefore, sustainable and durable. The representation of the “three pillars of sustainability” implies the fact that the concept of sustainability itself is the result of interactions between these three dimensions. That is the reason why they cannot, or rather should not, be analysed separately from each other (Rossi et al., 2012).

In response to the intersection of urban freight transport and the concept of sustainability, a holistic approach to globalize planning and urban management needs to be adopted (Robusté et al., 2000). Such a challenge needs to consider together all operations and services present in the city; special attention to the sustainability of the system should be paid. This new discipline, which aims at systemic or holistic optimization of city services, could be called Sustainable City Logistics.

Figure 1. Schematic view of the “three pillars of sustainability”.

1.2. Answering the questionsTherefore the question to answer in this roadmap is how to make city logistics sustainable. This question is very broad and covers many answers. That is why there has been an attempt at setting more specific objectives for our roadmap. Consquently, and for this purpose this work aims to solve two sub questions related to this capital aspect.

1.2.1. Are sustainability policies really sustainable?

The first sub question which is being addressed is about road freight transport. It is well known that road freight transport is causing a number of social, environmental and economic negative impacts in many cities around the world. Therefore sustainable city logistics must be the solution to the problems of urban centers, and researchers must have as their main objective to reduce these impacts without penalizing cities needs (Chang & Yen, 2012). Moreover, policy makers and decision makers aim at decreasing the above mentioned variety of negative social, environmental and economic impacts of urban freight transport. Because of this several initiatives and policies have been implemented to try reduce them (temporal regulation of access, promotion of cooperation between public and private sector, etc.). Some of the objectives of these policies are to improve the environment (air and noise quality), securing pedestrian’s space and the prevention of accidents. They all have sustainability as the ultimate goal (Dalkmann & Brannigan, 2007).

In this situation, City Logistics researches, reflect upon the impact of these policies on the different areas and upon the interests of the different stakeholders involved in urban areas and its centers (citizens, residents, merchants, transporters, local authorities, etc.). This is a field that has been investigated in recent years (Quak & de Koster, 2009; Gonzalez-Feliu et al., 2012; Stathopoulos et al., 2012). Given the heterogeneity of the interests of these stakeholders, coordination becomes somewhat cumbersome, so they generally act independently and without any centralized control. But this paper seeks to answer a less particular issue; a question which captures the overall interests of all stakeholders involved (general interests should be above individuals): are sustainability policies really sustainable? Therefore, the first purpose of this work is to evaluate one of these policies in a quantitative way to answer the question (Muñuzur et al., 2013).


Grosso-delaVega, R. and Muñuzuri, J.


1.2.2. How to make urban fleets more sustainable?

The second sub-question that arises from the main one is about the urban fleets, more specifically about the fleet in charge of recyclable waste.

Trying to solve the problem of waste collection in cities is not a new problem. Back in the 70’s, authors already addressed the problem, either from a mathematical point of view (Marks & Liebman, 1970), or by modeling and solving a vehicle routing problem (VRP) (Beltrami & Bodin, 1974; Turner & Hougland, 1975). This problem is not easy to solve since it falls under the classification of NP-hard.

The increased levels of consumption and the waste generation associated with it, the environmental considerations and the sustainability of cities have led to the emergence of new European and national policies regarding the management of municipal waste. An example of this is the National Integrated Waste Plan implemented in Spain in 2009, which is to continue the previous National Urban Waste Plan (PNRU). Among other things, it enforces municipalities over 5000 inhabitants to ensure proper separation for a selective collection of waste. Such measures imply the consideration of new challenges to municipalities, even more so in the economic recession framework in which we live. Different types of dumpsters, different types of waste, the location of dumpsters, pollution, energy consumption, cost reduction and the like, are some of these challenges. In this sense, authors address the problem from such perspectives as the consumption of fuel (Sonesson, 2000), or having in mind environmental and economic goals.

Nowadays the emergence of new technologies and the drop in their price allow researchers to find new tools to solve this problem. Examples of these new technologies are, among others, the Geographic Information System (GIS), volumetric sensors, or radio frequency identification (RFID). By using this technology, some issues may be addressed. These include eliminating unnecessary stops, fleet reduction and balancing according to demand, pollution impact reduction, operating costs reduction, etc. These issues are actually the basis of some research projects undertaken in recent years (Chang, Lu, & Wei, 1997; Nuortio et al., 2006) . Needlles to say that all these new lines of reseach offer a great potential for future work.

It is in this direction that this work moves. This part of the project addresses the problem of waste disposal in urban areas with the real-time level data

of the dumpsters. In particular, the work focuses on the collection of glass containers. A more sustainable collect policy is present and compared with other classical optimization algorithms (Grosso-delaVega et al., 2014).

2. Proposed SolutionsThe objectives of the roadmap will focus on:

- Characterize and analyse the situation of city logistics and characterize and analyze the situation of recyclable waste collection in the European Union and Spain.

- Study the existing scientific literature on city logistics and recyclable waste collection, especially in the field of sustainability and city centers.

- Study of the determining factors for sustainable development of city logistics in centers in European and the particularly factors in Spanish cities.

- Design optimization models for sustainable city logistics improvement and for better understanding and analysis.

- Development of a simulation environment, using heuristics and metaheuristics, specifically designed for City Logistic problems in city centers.

- Validation of the models proposed in the simulation environment.

As already mentioned above, the proposed methodology focuses on optimization algorithms. Also, also solutions need to found in a relatively short time; in this way fast optimization mechanisms such as metaheuristics, heuristics and techniques are implemented. These will be compared with existing techniques in order to be able to verify the hypothesis.

The work has been divided into four stages, which be conducted sequentially:

1. Study of the history of freight transport in Europe and the state of the art in terms of optimization of urban transport routes and its sustainability.

2. Development of a simulation environment in which to test the heuristics and metaheuristics.

3. Development and codification of the different heuristics and metaheuristics are considered to solve the said problems.




4. Analysis of heuristics raised. Study and compari-son of the results obtained. Analysis of the im-provements that the system would provide in a real environment.

3. Expected and existent contributions

This roadmap project was initiated in September 2012. Since then there have been many experiments and some intermediate results have been obtained. There are still results to be complete, however, although some of the responses to the issues raised have been published in the following papers derived from the roadmap: Grosso-delaVega et al. (2014) and Muñuzuri et al. (2013).

Other published works related to the main theme of the roadmap are: Muñuzuri et al. (2012a) and Muñuzuri et al. (2011).

In their paper, Muñuzuri et al., (2013) developed a model based on VRP logic called Vehicle Routing Problem with Access Time Windows (VRPATW). This model was solved using genetic algorithms. They provided conclusive results, about the sustainability of the policies adopted in the city centers. Following the line of research initiated earlier, the autors are currently working on the development and resolution of the model. It is intended to solve as a mathematical model and using a Greedy heuristic. In this way the model would be solved by mathematical programming, using a metaheuristic and also a heuristic. The aims are:

- To be able to answer the questions raised in a more precise way.

- Perform a comparison of the different techniques used in terms of methodology. This comparison is intended be accomplished in terms of:

o Proximity to the optimal solution

o Size of the problem that can be solved with each technique

o Solving times.

At present, this research project is at the design of the experiment phase stage. These experiments must be designed in order to be solved by means of the three techniques. It must be said that the greedy heuristic is being tested so that it solves the problem satisfactorily. With respect to the line of garbage collection, this research project is currently trying to improve the resolution algorithm in order to to improve the results.

At the time that this work was written had another year and a half to the end of the stipulated period of time for the finalization of the roadmap. Given the published results and the results that could be obtained, it is expected that two publications can be submitted in the period of time left.

Aditionally, potential contributions of the roadmap might include the following:

- A move from the theoretical level to the practical level and transfer the results of this roadmap to local authorities.

- Continue developing as a scientist.

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net.3230040106

Chang, N.-B., Lu, H., Wei, Y. (1997). GIS technology for vehicle routing and scheduling in solid waste collection systems. Journal of Environmental Engineering, 123(9): 901-910. doi:10.1061/(ASCE)0733-9372(1997)123:9(901)

Chang, T.-S., Yen, H.-M. (2012). City-courier routing and scheduling problems. European Journal of Operational Research, 223(2): 489-498. doi: 10.1016/j.ejor.2012.06.007

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Figliozzi, M. A. (2012). The time dependent vehicle routing problem with time windows: Benchmark problems, an efficient solution algorithm, and solution characteristics. Transportation Research Part E: Logistics and Transportation Review, 48(3): 616-636. doi: 10.1016/j.tre.2011.11.006


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http://dx.doi.org/10.1061/(ASCE)0733-9372(1997)123:9(901)

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Gonzalez-Feliu, J., Ambrosini, C., Pluvinet, P., Toilier, F., Routhier, J.-L. (2012). A simulation framework for evaluating the impacts of urban goods transport in terms of road occupancy. Journal of Computational Science, 3(4): 206-215. doi:10.1016/j.jocs.2012.04.003

Grosso-delaVega, R., Muñuzuri Sanz, J., Rodriguez Palero, M., Teba Fernandez, J. (2014). Optimization of recyclable waste collection using real-time information. Annals of Industrial Engineering 2012 (pp. 171-177): Springer.

Ligocki, C., Zonn, L. E. (1984). Parking problems in central business districts. Cities, 1(4): 350-355. doi:10.1016/0264-2751(84)90006-4

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May, A. D. (2013). Urban transport and sustainability: The key challenges. International journal of sustainable transportation, 7(3): 170-185. doi:15568318.2013.710136

Muñuzuri, J., Cortés, P., Grosso, R., Guadix, J. (2012a). Selecting the location of minihubs for freight delivery in congested downtown areas. Journal of Computational Science, 3(4): 228-237. doi: 10.1016/j.jocs.2011.12.002

Muñuzuri, J., Cortés, P., Guadix, J., Onieva, L. (2012b). City logistics in Spain: Why it might never work. Cities, 29(2): 133-141. doi: 10.1016/j.cities.2011.03.004

Muñuzuri, J., Grosso, R., Cortés, P., Guadix, J. (2011). Development of a Cost Model for Intermodal Transport in Spain. In S. Renko (Ed.), Supply Chain Management - New Perspectives. doi:10.5772/22740

Muñuzuri, J., Grosso, R., Cortés, P., Guadix, J. (2013). Estimating the extra costs imposed on delivery vehicles using access time windows in a city. Computers, Environment and Urban Systems, 41: 262-275. doi: 10.1016/j.compenvurbsys.2012.05.005

Muñuzuri, J., Larrañeta, J., Onieva, L., Cortés, P. (2005). Solutions applicable by local administrations for urban logistics improvement. Cities, 22(1): 15-28. doi: 10.1016/j.cities.2004.10.003

Nuortio, T., Kytöjoki, J., Niska, H., Bräysy, O. (2006). Improved route planning and scheduling of waste collection and transport. Expert Systems with Applications, 30(2): 223-232. doi: 10.1016/j.eswa.2005.07.009

Quak, H. J., de Koster, M. B. M. (2009). Delivering Goods in Urban Areas: How to Deal with Urban Policy Restrictions and the Environment. Transportation Science, 43(2): 211-227. doi: 10.1287/trsc.1080.0235

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http://dx.doi.org/10.1016/j.techsoc.2005.10.007

http://dx.doi.org/10.1016/j.techsoc.2005.10.007

http://dx.doi.org/10.1016/j.jocs.2012.04.003

http://dx.doi.org/10.1016/0264-2751(84)90006-4

http://dx.doi.org/10.1080/15568318.2013.710136

http://dx.doi.org/10.1016/j.jocs.2011.12.002

http://dx.doi.org/10.1016/j.cities.2011.03.004


http://dx.doi.org/10.5772/22740

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http://dx.doi.org/10.1016/j.eswa.2005.07.009

http://dx.doi.org/10.1287/trsc.1080.0235

http://dx.doi.org/10.1007/s12544-012-0086-5

http://dx.doi.org/10.1177/0734242X0001800203

http://dx.doi.org/10.1016/j.jtrangeo.2011.11.017

http://dx.doi.org/10.1080/05695557508975027


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Fuzzy maintenance costs of a wind turbine pitch control device

Mariana Carvalhoa,b, Eusébio Nunesb,i and José Telhadab,ii

a Polytechnic Institute of Cávado and Ave, Barcelos, Portugala [email protected]

b Centro Algoritmi, University of Minho, Braga, Portugalb,i [email protected]

b,ii [email protected]

abstract: This paper deals with the problem of estimation maintenance costs for the case of the pitch controls system of wind farms turbines. Previous investigations have estimated these costs as (traditional) “crisp” values, simply ignoring the uncertainty nature of data and information available. This paper purposes an extended version of the estimation model by making use of the Fuzzy Set Theory. The results alert decision-makers to consequent uncertainty of the estimations along with their overall level, thus improving the information given to the maintenance support system.

Key words: Wind turbine, Pitch Control, Maintenance cost, Fuzzy sets.

1. Introduction

Wind power technology is one of the major growing areas in the energy sector. In a few years’ time wind power has gone from a minor energy source to a large-scale industry. Proper and well-planned service and maintenance strategies are very important to ensure an efficient energy production and required to effectively reduce the costs associated with Wind Turbine (WT) support.

Maintenance management approaches aim to find a sound balance between costs and benefits of performing maintenance. Some experiments and studies show that there is a large potential to reduce overall costs in the maintenance of WTs (e.g. Bertling et al., 2006).

According to Morthorst (2003), Operation and Maintenance (O&M) costs constitute a sizeable share of the total annual costs of a WT. For a new machine, O&M costs might easily have an average share over the lifetime of the WT of approximately 20% to 25% of the total cost per kWh produced. In an attempt to shed some light on this problem, other works (e.g. Carvalho et al., 2013a) has been focused on studying the active power control system or pitch control system of WTs.

This system assumes primordial importance in the wind turbine, because: i) it is crucial in the optimization of the turbine efficiency; ii) it is very important with regard to the safety of the turbine (Naranjo et al., 2011); and iii) reveals frequent failures and large residence time in failure state compared to other systems of the machine (Nilsson & Bertling, 2007; Carvalho et al., 2013b). Consequently, to guarantee a normal operation, they are usually needs maintenance actions, which are only provided by the manufacturer (Teresa, 2007). Moreover, information related to failure modes, (un)availability and maintenance costs of these systems remain confidential and only the manufacturer has knowledge about them. This situation does not facilitate, for example, the work of company managers who search for better warranty and maintenance contracts.

In complex systems, such as pitch control systems, the maintenance management function is commonly supported by analyses of collected data as well as on the quality and experience of maintenance engineers and others experts in this field. In this context, this function is often very difficult, and unrealistic decisions come out from the process with undesirable frequency. So, it is expected that the Fuzzy Set Theory, applied in the maintenance management, will lead to more realistic decisions.




The study presented in this paper is based on an analysis of two years data collected from 21 identical WTs installed in a wind farm in Portugal. The data were provided confidentially by the company that manages the wind farm. For this reason, the name of the company and the WT brand are not revealed. Each WT under analysis has a nominal power of 2 MW, three rotor blades and an active power control (pitch).

The main objective of the study consists on reporting the gathering process of information about WT functioning and its failures and costs, and conducting some reliability analyses, providing an estimate of the associated maintenance costs of the pitch system.

The remainder part of the paper is organized as follows. Section 2 introduces the fundamentals of the Fuzzy Set Theory. Section 3 describes the system under study, the pitch control of the WTs, and its fault and error states. In Section 4, it is proposed a model for the fuzzy maintenance costs of the pitch control system. Section 5 reports the results of the application of the proposed model and discusses its practical relevance. Finally, the main conclusions of this study are discussed in Section 6.

2. Fuzzy Set Theory

2.1. IntroductionThe Fuzzy Set Theory has been extensively studied in the past 30 years, largely motivated by the need for a more expressive mathematical structure to deal with human factors. This theory has a major impact on industrial engineering and maintenance manage-ment systems. During the last decade, several models for maintenance management problems have been incorporating uncertainty of their parameters by us-ing fuzzy sets (e.g. Yuniarto and Labib, 2006; Khan-lari et al., 2008; Sharma et al., 2008; Shen et al., 2009). Nevertheless, most of the current literature on maintenance simply omits the uncertainty that is in-herent to real processes. Fuzzy sets are adequate, for instance, to estimate the lifetime or the failure rate of a given equipment that operates in different environ-ments. In most cases, statements in plain language may be the best form to express the knowledge about a system. However, this information is naturally very inaccurate and any realistic estimate inferred from that is always an approximation.

2.2. Basic ConceptsA fuzzy set A, in the universe of discourse X, is defined by a membership function, μA(x): X→[0,1], which assigns, for each element of X, a membership degree to A.

Definition 1: Given a fuzzy set A defined on X and any number αÎ(0, 1], the α-cut set, Aα, is the crisp set expressed by Eq. (1).

Aα ={x: A(x) ≥ α} (1)

The α-cut set concept allows us to manipulate fuzzy sets by using the interval arithmetic. Alternatively, such manipulation can be performed by the extension principle introduced by Zadeh (1975). This is an important tool by which classical mathematical theories can be fuzzified. On the other side, defuzzification is the conversion of a fuzzy quantity to a crisp quantity. Despite the fact that the bulk of the information emerging every day is fuzzy, most of the actions or decisions implemented by humans or machines are crisp or binary. A detailed application of defuzzification methods can be found in Klir and Yuan (1995).

Among the innumerous types of fuzzy sets, those that are defined in the set of the real numbers assume a particular importance. These sets have a quantitative meaning and under certain conditions they can be treated as fuzzy numbers, e.g. when intuitively and linguistically they represent approximate numbers, such as “the preventive maintenance duration is around τ hours” (Ross 1995).

In reliability and maintenance studies, the triangular and trapezoidal numbers are the most used number patterns because their simplicity and adequacy on representing uncertainty, vagueness and subjectivity of data and human judgment. Without loss of generality, this paper only deals with triangular fuzzy numbers. A triangular fuzzy number, A, can be defined by a triplet (a1, a2, a3), where μA(a1) = μA(a3) = 0 and μA(a2) = 1. Each α-cut, Aα, is a closed interval represented as Aα = [a1

α, a3α], where:

a1α = (a2 – a1) α + a1

a3α = a3 – (a3 – a2) α (2)

The family of cut sets {Aα: αÎ(0, 1]} is a representation of the fuzzy number A. An illustrative graphic representation of A is shown in Figure 1.


Carvalho, M., Nunes, E. and Telhada, J.


X1αa

1

α

Aµ

A

a1 a3a2 3αa

Figure 1. Representation of a triangular fuzzy number.

Like functions, f, are important in mathematical modeling, fuzzy functions, F̃, are important in fuzzy modeling. The usual way of obtaining a fuzzy function is to extend a function to map fuzzy sets to fuzzy sets. There are two common methods to accomplish this extension: the extension principle procedure; and the α-cut and interval arithmetic procedure. This paper uses the extension principle (Zadeh 1975). The extension principle may be generalized to functions of many independent variables X1, X2,…, Xn (Buckley & Eslami, 2002).

Let A1, A2,…, An be triangular fuzzy numbers of X1, X2,…, Xn respectively, represented by the α-cuts: A1

α=[a11α, a13

α], A2α=[a21

α, a23α],…., An

α=[an1α, an3

α]. Using the extension principle, it is possible to extend f to F ̃, where B=F(A1, …, An). If Bα=[b1

α, b3

α], then

b1α=min{f(x1, …, xn): x1∈A1

α, …, xn∈Anα}

b3α=max{f(x1, …, xn): x1∈A1

α, …, xn∈Anα}

Note that min and max can be used in these equations, for the reason that a continuous function in a closed interval takes its maximum and minimum. Therefore, if there are two triangular fuzzy numbers, A1 and A2, and supposing that:

∂f ⁄ ∂x1>0 and ∂f ⁄ ∂x2<0

that is, f is an increasing (decreasing) function in x1(x2), hence, for all α:

b1α = f(a11

α, a21α ) and b3

α=f(a13α,a23

α ) (3)

3. Pitch control deviceThe main purpose of a pitch system is to prevent the power of the electric generator from being exceeded in the case of high wind speeds, as well

as preventing relieve strain on the structure and components of the wind turbine. This system acts on the aerodynamic forces by controlling the loads and power.

In the active control, blades may undergo rotation about its longitudinal axis, which makes it changes the angle of attack of the blades with respect to the relative wind speed. This process takes place through hydraulic or electric systems, which respond to an electronic control which checks the power output. Whenever the power is too high, the control triggers the mechanism. The main advantages of this system are related to its capability to limit the power for high wind speeds, facilitating the start-up operation, to diminish the efforts and to optimize power when the turbine is operating at partial load. The pitch system also assumes primordial importance with regard to the safety of the turbine. A flaw in this system, combined with adverse climatic situation (e.g. a storm) may lead to an uncontrolled rotation speed of the blades and catastrophic consequences, including, in the limit, a total destruction of the turbine. The states that actively influence the reliability of the active control power are:

Fault blade load control (State S1): the control effort in the turbine is constantly monitored. This state means that an undue effort has been exercised in the blade. The wind turbine is still operating, but it is under reduced power. The maintenance service has to rectify stress effects. This state actively influences the state S2.

Pitch control error (State S2): The angles of the three blades are continuously monitored. When there is a difference in the angles of the blades (can be erroneously due to a measurement error), state S2 arises, which leads to a shutdown of the turbine and the engine restarts automatically. If the problem persists for a predefined number of times, the maintenance service will have to repair the fault.

4. Fuzzy maintenance costs of the pitch control device

Recent studies have been emphasizing the importance of the pitch system for the functioning, cost optimization and security of the wind turbine (Carvalho et al., 2013a). In this study it was very difficult to estimate the maintenance costs related to these two states, S1 and S2. The wind farm company only knows the information that can be observed from the data made available to this study. From that, with relevance to the analysis of




maintenance costs, one can highlight the record of the exact time of occurrence of each state in each turbine and the wind speed at the time of occurrence. Such information allows estimating the downtime cost, for states S1 and S2, as well as the number of preventive and corrective maintenances carried out in two years. However, the costs of corrective and preventive maintenance, and the number of replacements made, were not revealed by the WT manufacturer (who performs maintenances as well). One can only know approximate values from the experience of managing experts from this and from others wind farms who were consulted in the context of this work. Therefore, part of the information collected does not follow statistical analysis, but rather statements of experts, based on their knowledge and experience and, consequently, they are subject to an increased level of uncertainty.

4.1. Maintenance costsKnowing the costs of maintenance, albeit mere approximations, allows the wind farm company to make better decisions, particularly with regard to contracts for the maintenance established with the manufacturer. However, this information either does not exist or is not public.

In the context of this study, the contract that the wind farm company has with the manufacturer assumes the execution of four interventions per year in each turbine, conducted at quarterly intervals. Specifically, the manufacturer performs an electrical preventive maintenance, a mechanical preventive maintenance, a visual inspection and a lubrication operation. The manufacturer is also responsible for any corrective maintenance that is necessary, as well as some improvement maintenance he may consider as fundamental. This maintenance provided by the manufacturer is a necessary condition to offer warranty to the wind farm company. Associated costs are 38000€ per year per turbine by 15 years. In reality, it is not possible to know the exact cost for each preventive and corrective maintenance, since the maintenance contract does not explicit these costs.

4.1.1. Costs of unavailability Table 1 summarizes the frequency, duration and cost of the resulting unavailability of states S1 and S2, for the 21 turbines, in the two years.

Table 1. Resume of the effects of states S1 and S2 for the 21 machines in two years.

StateS1 S2

N.º of occurrences 196 431Unavailable time (hh:mm:ss) 949:55:08 1609:32:52Unavailable cost (€) 108276.40 129733.20Average unavailable time (h) 5 4Average unavailable Cost (€) 552 301

The cost of downtime shown in the last row of Table 1 was estimated as a function of wind speed records and the ratio of power with wind speed, displayed in Figure 2.

Figure 2. Power curve as a function of wind speed.

The data of average wind speed and the respective wasted power by the turbine, and other details, resulting from the appearance of these two states can be found in Qiu et al. (2012).

The energy wasted by the occurrence of each state is given by:

Energy [MWh]= (Power[kW]×MDT[h])/1000

where the mean downtime (MDT) is the average time that a system is non-operational for being either in state S1 or state S2. The cost of down-time was estimated supposing that the energy produced is sold at 90€ per MWh. More details about this estimation can be found in Carvalho et al. (2013b).

4.1.2. Preventive maintenance costsFor the preventive maintenance of the pitch system, experts mentioned that a preventive maintenance of the active power control system costs at least 580€ and requires about 4 hours, which additionally




represents an approximate unavailability cost of 293€ due to preventive maintenance, assuming an average wind speed of 8 m/sec.

4.1.3. Corrective maintenance costsAnalyzing data from the 431 recorded instances of state S2, it resulted in 53 repairs, i.e. 12.3% of cases requiring maintenance. The maintenance usually consists in replacing the engine of blades. To ensure machine availability, repairs are never made on site. The engine is replaced and thereafter is repaired in the manufacturer. The experts indicate that the cost of each engine is around 2000€. Among the 196 occurrences of state S1, 46 triggered a corrective maintenance, which corresponds to 23.5% of all cases. When the maintenance team makes an intervention in the failure load control (S1), usually they replace the sensors that somehow quantify the load exerted on the blade. A new sensor costs around 50€.

4.2. Total maintenance costsThe total maintenance cost of the active power control system for the 21 machines in the two years can be given by Eq. (4).

C=CCMS1×NCMS1+CCMS2×NCMS2+2×4×21× ×(CPM+CUPM)+NOS1×CUS1+NOS2×CUS2 (4)

where:

C: total cost of maintenance of the active power control system for the 21 machines in the two years;CCMS1: corrective maintenance cost of state S1;CCMS2: corrective maintenance cost of state S2;CPM: preventive maintenance cost of pitch sytem;CUPM: unavailability cost, due to preventive maintenance;CUS1: average unavailability cost, due to state S1;CUS2: average unavailability cost, due to state S2;NCMS1: number of corrective maintenance of the state S1;NCMS2: number of corrective maintenance of the state S2;NOS1: number of occurrences of the state S1;NOS2: number of occurrences of the state S2.

Table 2 presents estimates for the values of these parameters. These estimates were calculated from data provided by the management of the wind farm and information obtained from interviews with managers of the park. Applying these values in Eq. (4), it was estimated the amount of 492,887€ to

the cost spent on pitch system maintenance of the 21 turbines of the wind farm, in the two years under review.

Thus, on average, the annual maintenance cost of each active power control system was around 11735€. Note that the only available information for the maintenance cost is that which prevails at the contract between the company and the manufacturer, i.e. 38000€ per year per turbine.

As mentioned above, the true costs of corrective and preventive maintenance, and the number of replacements made, were not revealed by the WT manufacturer. Thus the total maintenance cost obtained by Eq. (4) contain a certain level of uncertainty which depend of their parameters uncertainty.

Some issues may arise at this point, such as: What is the confidence level for the total value of the maintenance cost obtained by Eq. (4)? How to represent non-probabilistic uncertainty present in some of the cost components? How the uncertainty in the cost components affect the uncertainty in the total cost of maintenance?

In the following section, these issues will be addressed using the theory of fuzzy sets introduced above, in Section 2.

5. Fuzzy total maintenance cost analysis

Consider the same parameters of Eq. (4), but admit now that the uncertainty inherent to the following parameters must be not neglected: preventive maintenance cost, CPM, and unavailable costs due to preventive maintenance, CUPM. Estimates for these costs (Table 2) have a very fragile analytical basis due to limited access to the data (these are not provided by the companies providing maintenance services to the park), so it is assumed that the uncertainty associated with these costs is high. The preventive maintenance cost, CPM, for example, is exclusively known by the experts’ opinion. The unavailable costs due to preventive maintenance, CUPM, are also very uncertainty, because it is assumed an average wind speed of 8 m/s.

The uncertainty associated with these parameters was appraised from the great experience and indispensable collaboration of two managers of the park. By consensus, the managers presented, for each of these cost parameters, the value they considered most plausible, and the values below and




above from which they consider as impossible to occur. Based on this information, is was set up the fuzzy triangular numbers for CPM and CUPM. Table 3 shows the parameters of the Eq. (4), assuming those as triangular fuzzy numbers.

Table 3. Crisp and fuzzy parameters estimates of the maintenance cost function.

Parameter Estimative (€)CCMS1 50CCMS2 2000C̃PM (450, 580, 800)C̃UPM (180, 293, 350)CUS1 552CUS2 301

Eq. (4) can now be rewritten as:

C̃= CCMS1×NCMS1+CCMS2×NCMS2+2×4×21× ×(C̃PM+C̃UPM)+NOS1×CUS1+NOS2×CUS2 (5)

Using the extension principle, C extends to C̃, where C̃=C(CP̃M, C̃UPM, CCMS1, … ).

If Cα = [c1α, c3

α], by Eq. (2) and Eq. (3) results:

c1α =C[(580–450)α+450, (293–180)α+180, 50,…]

and

c3α=C[800–(800–580)α, 350–(350–293)α, 50,…]

Then, by Eq. (5), the maintenance cost will be between 452,063€ and 539,423€.

These values determine the confidence interval of the total maintenance cost C (universe of discourse C). The higher the magnitude of this interval, the greater is the uncertainty present in the cost. Figure 3 represents this result graphically, as a triangular fuzzy number.

452063 492887 539423Cost €

0.2

0.4

0.6

0.8

1a

Figure 3. Fuzzy maintenance cost.

By determining the mean total cost for each turbine, CW̃T, it is obtained the fuzzy number:

CW̃T =C̃/21×2=(452063, 492 887, 539423)/42 = (10763, 11735, 12843)

This approach seems to give rise to more realistic solutions, allowing for better decisions in decision making processes. On the other hand, the difficulty of interpreting the results increases. These difficulties are due to the large quantity of possible outcomes for CWT, represented by the universe of discourse of C̃WT. The way to reduce uncertainty in the value of CWT involves obtaining more information about CPM and CUPM, thus reducing the universe of discourse of the fuzzy parameters C̃PM and C̃UPM.

Figure 4 shows the fuzzy maintenance cost, C̃, when the universe of discourse of C̃PM and C̃UPM is reduced by 30%. In this case, we had set C̃PM=(490, 580, 735) and C̃UPM=(210, 293, 330).

It is thus noted that the uncertainty reduction of about 30% of C̃PM and C̃UPM leads to the same level of reduction in the uncertainty of C̃.

463823 492887 525143Cost €

0.2

0.4

0.6

0.8

1a

Figure 4. Fuzzy maintenance cost (reducing uncertainty).

Frequently, the membership function is defuzzificated to obtain a crisp number. However, a lot of information that can be relevant to the decision process is lost in the defuzzification operation. That is, the fuzzy result is richer than the crisp result, and the former should be preferred whenever possible.

6. ConclusionsIn complex systems it is impossible to has a perfect knowledge about the involved parameters (failure rates, unavailability times, etc.) and about their interdependency relationships. Considering these results as “crisp” values is equivalent to assume that there is no uncertainty in these data. But, in fact, the uncertainty of data is intrinsic to the system and it is not probabilistic. To overcome these limitations, the application of the Fuzzy Set Theory proves to be




an interesting approach for capturing the vagueness and fuzziness of the cost parameters. The application of this theory allows to propagate the uncertainty from parameters to results in the modelling process. The study reported in this paper has demonstrated the validity of these conclusions in the case of a particular maintenance cost problem. Moreover, the proposed fuzzy modelling approach will allow managers to make their decisions based on a reacher set of information than that they would have by the

application of tradicional crisp valued parameters approach.

Acknowledgements

This work was financed with FEDER Funds by Programa Operacional Fatores de Competitividade – COMPETE and by National Funds by FCT –Fundação para a Ciência e Tecnologia, Project: FCOMP-01-0124-FEDER-022674.

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on condition monitoring systems. Elforsk report 06:39, May 2006.

Buckley, J. J., Eslami, E. (2002). An Introduction to Fuzzy Logic and Fuzzy Sets. Physica-Verlag, Heidelberg, New York. doi:10.1007/978-3-7908-1799-7

Carvalho, M., Nunes, E., Telhada, J. (2013a). Maintenance costs of a pitch control device of a wind turbine. Proceedings of the World Congress on Engineering 2013, July 3-5, London, 569-574.

Carvalho, M., Nunes, E., Telhada, J. (2013b). State-space characterization and estimation of unavailability costs of a wind turbine. Proceedings of the International Conference on Industrial Engineering and Operation Management 2013, July 10-12, Valladolid, Spain.

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Nunes, E., Faria, J., Matos, M. (2006). Using fuzzy sets to evaluate the performance of complex systems when parameters are uncertain. Safety and Reliability for Managing Risk. In C. Guedes Soares and E. Zio (Eds.). Proceedings of the ESREL 2006, 3: 2351-2359. Estoril, Portugal, Taylor & Francis Group.

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http://dx.doi.org/10.1007/978-3-7908-1799-7

http://dx.doi.org/10.1007/978-3-7908-1799-7

http://dx.doi.org/10.1016/j.ins.2005.06.008

http://dx.doi.org/10.1016/j.cie.2007.07.002

http://dx.doi.org/10.1002/etep.535

http://dx.doi.org/10.1109/TEC.2006.889623

http://dx.doi.org/10.1002/we.513

http://dx.doi.org/10.1080/00207720701717708

http://dx.doi.org/10.1007/s00500-008-0307-y

http://dx.doi.org/10.1007/s00500-008-0307-y

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http://dx.doi.org/10.1016/0020-0255(75)90017-1


PME

IJ





Which of DEA or AHP can best be employed to measure efficiency of projects?

Marisa A. Sánchez Departamento de Ciencias de la Administración, Universidad Nacional del Sur

Campus Palihue, Bahía Blanca, Argentina [email protected]

abstract: This paper compares Analytic Hierarchy Process (AHP) and Data Envelopment Analysis (DEA) approaches for monitoring projects, in order to determine their performance in terms of economic, environmental and social organizational goals. This work is founded on an existing methodology to select and monitor projects based on DEA, and discusses modifications and additions arising from using AHP. The proposal is applied to a real case. The results indicate that AHP constitutes an insightful approach in situations requiring a modelling of managerial preferences regarding the relative importance of organizational goals.

Key words: Analytic Hierarchy Process, Project Management, Sustainability, Data Envelopment Analysis.

1. IntroductionConsumers and regulators exert continuous pressure on firms to innovate in ways that will reduce their impact on the natural environment (Yalabik and Fairchild, 2011). Porter argues that ‘for profit’ companies are well suited to solve social problems while at the same time serving their shareholder’s interest to maximize investor returns (Porter and Kramer, 2011). Executives face the challenge of balancing sustainability related to the whole strategic priorities. This requires an effective project portfolio management that is supportive of sustainability driven strategies.

In this paper we consider the problem of assessing projects so that they provide maximum value and minimize environmental and social impacts. The main challenges are evaluating projects that support different goals, some of them provide benefits that cannot be measured in monetary terms, and prioritize them together with the existing company´s portfolio. In a previous work (Sánchez, 2014) an approach that integrates sustainability into project management is proposed. Data Envelopment Analysis is used for selection and monitoring of projects.

DEA is widely recognized as an effective means of evaluating the relative efficiency of a group of homogeneous decision making units which produce multiple outputs by using multiple inputs. However, there are some drawbacks. Managers need to assess projects considering different scenarios due to uncertainty. Joro and Viitala (2004) note that all inputs and outputs may not be equally relevant to the organizations analyzed and their stakeholders. And hence, it is useful to assign preferences to organizational goals or costs. However, by using DEA goals are modeled as having the same preference. Another issue with DEA is the homogeneity assumption where all DMUs are required to undertake the same processes, they should use the same inputs to produce the same outputs, and it is required that they operate within the same environment (Mar, 2009). Then, a very large unit is deemed efficient because there are no other units with similar production levels (Madlener et al., 2006). As a consequence, DEA would prevent direct efficiency comparisons between small and large projects.

In particular, this research addresses the project monitoring problem through the integration of AHP into the project management framework presented in the work of Sánchez (2014). The method is based




on a multi-criteria decision making technique that allows incorporating preferences among criteria. Hence, the research question becomes ‘which of DEA or AHP can best be employed to measure efficiency of projects and are decisions derived from DEA and AHP consistent?’ In order to answer this research question we describe how to develop an AHP-based model to monitor projects. The proposal is applied to a case study and results are compared with rankings produced by DEA.

This work is organized in the following sections. Section 2 introduces background concepts such as DEA and AHP. Section 3 describes the AHP model and discusses issues such as how to model costs and benefits. Section 4 describes the results obtained when using the different methods. Section 5 concludes with general findings on the applicability and consistency of the methodologies.

2. Theoretical background

2.1. Data Envelopment AnalysisDEA, first proposed by Charnes (Charnes et al., 1978), is a non-parametric technique used to measure the efficiency of Decision Making Units (DMUs). Each DMU is seen as being engaged in a transformation process, in which, some inputs (resources) are used to try to produce some outputs (goods or services). In management contexts, DEA serves as a tool for control and evaluation of past accomplishments as well as a tool to aid in planning future activities (Banker et al., 1984; Cook and Seiford, 2009). DEA models return an efficient projection point of operation on the frontier for each inefficient DMU, thus identifying the DMUs that can be used as performance benchmarks for the DMUs that are operating inefficiently.

There are some limitations regarding the application of DEA as described in Section 1. Another issue of using DEA as a benchmarking tool is that it may provide inappropriate benchmark DMUs for inefficient DMUs when the inputs (or outputs) are derived from two distinct objectives (Camanho and Dyson, 1999; Shimshak et al., 2009; Chang and Yang, 2010). For instance, when quality outputs and operating outputs are directly mixed together for executing DEA. DMUs with low quality outputs (or low operating outputs) can be recognized as benchmark DMUs. This happens because DEA is linear-programming-based technique for evaluating efficiency of each DMU by selecting the most

beneficial weights for inputs and outputs; once the outputs are sufficiently high, the low outputs of another one may be ignored due to zero weights.

2.2. Analytic Hierarchy ProcessThe AHP allows decision makers to model a complex problem in a hierarchical structure showing the relationships of the goal, objectives (criteria), sub-objectives, and alternatives (Saaty, 1997). After arranging the problem in a hierarchical fashion, the decision-maker makes subjective assessments with respect to the relative importance of each of the criteria, and indicates the preference of each alternative with respect to each of the criteria. Comparison matrices are used for pairwise comparisons between the sub-criteria with respect to its parent node, and each pair of alternatives with respect to each sub-criterion. These comparisons may be taken from actual measurements or from a scale that reflects the relative strength of preference, relevance or probability. Given n criteria and m alternatives, n matrices of order m×m and order n×n should be built, which makes that AHP is a non-scalable method. Once judgments have been entered for each part of the model, the information is synthesized to rank the alternatives in relation to the overall goal.

2.3. AHP and Project ManagementKumar (2004) developed a model based on AHP for project selection. The criteria are structured using a pre-defined list of organizational, technical, strategic and financial factors. Pairwise comparisons of factors reflect the importance of each of them. Candidate projects are evaluated using a grading scale with five elements. In Kendrick and Saaty (2007) the authors propose a four-step process to define a portfolio. A hierarchy of business drivers is defined. Projects are rated against criteria and the priority of each project is represented as a measure of its relative value toward the stated goals and objectives of the organization. The ratings are derived through pairwise comparisons. Finally, optimization techniques are use to define the portfolio that maximizes value for cost based on business rules. The portfolio value measures the overall aggregated priority of all the projects that are funded.

In Bible and Bivins (2011) the authors provide a project selection method based on the AHP. The objectives hierarchy in the evaluation model is a representation of the strategic plan. An alignment matrix shows which candidate projects support


Sánchez, M. A.


which objectives from the strategic plan (but not how much or how well). To produce accurate priorities for the alternatives (candidate projects) with respect to the objectives they support, alternatives can be evaluated using pairwise comparisons, or by using absolute measurement scales. Pairwise comparisons can be cumbersome and time consuming. Absolute measurement scales are based on a point scale with assigned intensity levels for each point. Synthesized results of the evaluation provide the relative priorities of the candidate projects with respect to achieving a goal. The list of prioritized projects is used as the input to derive a portfolio using an optimization algorithm (the combination of projects that provides the greatest benefit for the budget level).

2.4. Project Management FrameworkThe methodology comprises four steps: (1) cover stakeholders’ concerns by means of stakeholder analysis; (2) define a Strategy Map and a Balanced Scorecard; (3) conduct sustainability analysis; and (4) perform a global optimization of projects (Sánchez, 2014). The full description of the framework exceeds the goals and scope of this paper. In what follows, a brief description of the main steps is provided.

The tasks involved in stakeholder analysis include identifying stakeholders and their interests. As a result of the analysis, stakeholders’ interests are translated into goals, and a Balanced Scorecard (BSC) (Kaplan and Norton, 2004) is drawn. The BSC is structured using four perspectives: Triple Bottom Line, Stakeholders, the Internal Process and Learning and Growth. The Triple Bottom Line perspective includes economic, environmental and social value goals. The Stakeholders perspective balances the interests of all stakeholders. To meet stakeholders’ expectations, the Internal Process perspective defines goals for processes. Finally, the Learning and Growth perspective includes goals related with the skills, culture and technology necessary for its employees to do the required work. For each goal, key performance indicators (KPIs) are described. Then, actions plans and projects are defined. The Strategy Map links together several domains and elements of the strategy in the four key perspectives.

The technique used to assess the environmental impact of projects depends on the characteristics of the project (e.g. production or service project).

The portfolio selection is formulated as a DEA problem where DMUs represent portfolios; inputs represent initial investments, development, operational and

disposal costs, and socio-environmental impacts derived from sustainability analysis; outputs represent the estimated contribution of portfolios to each goal. In this way, DEA results provide a ranking of portfolios based on the simultaneous analysis of eco-impacts and contribution to organizational goals. Similarly, project monitoring is represented as a DEA problem where each project defines two DMUs: one DMU represents the ongoing projects and input and output data are given by incurred costs and by realized value, or updated cost forecasts and value if the project is not closed. The other DMU represents the planned project and input and output data are given by initial estimated expenditures and expected value contribution. Ideally, DMUs representing planned projects would define the efficient frontier and would be the reference set for ongoing projects.

3. Portfolio selection and project monitoring based on AHP

The aim of this section is to describe how to perform the portfolio selection and project tracking task using AHP. The proposal is aimed to be used instead of DEA. The multi-criteria analysis should include criteria to represent goals defined in the BSC, and economic, environmental, and social costs that arise of project implementation. AHP has been adapted to perform a benefit/cost analysis. In its more general form, two AHP hierarchies are used for the same set of alternatives, one for benefits and the other for costs (Azis, 1990; Saaty and Ozdemir, 2003). After synthesis of information, the benefits’ priorities are then compared to the costs’ priorities to see which option has the highest ratio. A final ranking is calculated using the following expression (Saaty and Ozdemir, 2003):

A B Rmaxi i C1

i$= ^ h (1)

where Ai represents alternative i, Bi are the benefits of alternative Ai , Ci are the costs of alternative Ai , and Rmax represents the maximum of 1/Ci, 1≤ i ≤ n, where n is the number of alternatives.

3.1. Portfolio Selection

3.1.1. Benefit HierarchyCriteria representing benefits are derived from goals defined in the BSC. These criteria are structured in a benefit hierarchy. Each perspective in the BSC defines a branch in the hierarchy, and goals and sub-




goals provide criteria and sub-criteria. Finally, KPIs are represented at the preliminary final level of the hierarchy. Alternatives are given by candidate portfolios (see Figure 1) and are presented at the final level of the hierarchy.

Figure 1. Benefit hierarchy template for Portfolio Selection.

Once the hierarchy has been stated, the relative preference of each criterion is defined by performing pairwise comparisons. For the higher part of the hierarchy, the evaluation of the importance of the criteria and sub-criteria refers to management concerns. Judgments will be expressed by managers of different areas (e.g. project management, financial, marketing) according to their requirements.

In the case of alternatives, assessment does not consider preferences but the evaluation is based on the forecasted contribution toward each KPI if the portfolio is funded. A portfolio is composed of projects and the portfolio contribution to a KPI is given by the maximum project contribution. Assume there are z candidate portfolios. Let P={Pi ,1 ≤ i ≤z} be the set of portfolios. Let Pi={pk

i ,1 ≤ k ≤ ni}

denote the projects in portfolio Pi where ni is the number of projects and 1 ≤ i ≤ z. The contribution of project pk

i to each criterion is denoted by

Bki ={bk

i j, 1 ≤ j ≤ w} (2)

where w is the number of criteria. Hence, the contribution of portfolio Pi to each criterion is described by

Bi ={bi

max,j , 1 ≤ j ≤ w} (3)

where bimax,j represents the maximum of bi

k,j, 1 ≤ k ≤ ni. If a portfolio does not contribute to improve a particular KPI, then the current value of the KPI is used in the analysis.

Then, alternatives assessment is performed using raw data. Priorities can be derived from data as well as from pairwise comparisons (Forman, 2001) assuming a linear or inverse linear relationship is deemed to be reasonable. Simple arithmetic is adequate to derive the priorities by adding up each alternative data value, and dividing by the total to normalize such that the priorities add up to one. Similarly, inverse relationships can be calculated when a higher data value is less desirable.

3.1.2. Cost HierarchyCriteria representing costs are derived from economic and financial analysis developed for each candidate project in the portfolio. Additionally, environmental costs defined during the sustainability analysis step provide criteria and data. The relative preference of each cost is performed using pairwise comparisons. Alternatives assessment is performed using raw data (see Figure 2).

The portfolio cost measures the overall aggregated cost of all projects. More formally, the costs of project pk

i is denoted by

Cki ={ck

ij , 1 ≤ j ≤ q} (4)

where q is the number of cost categories. Hence, the cost of portfolio Pi is described by

,C c c j q1iji i

kv1 # #= = = kj

i" ,/ (5)

Figure 2. Cost hierarchy template for Portfolio Selection.

3.2. Project Monitoring

3.2.1. Benefit HierarchyThe benefit hierarchy defined during portfolio selection is updated according to changes in the


Sánchez, M. A.


BSC. Hence, some goals and KPIs may be added and others may be eliminated. Alternatives are given by new proposals and ongoing projects (see Figure 3) and are presented at the final level of the hierarchy.

If there is a change in the hierarchy defined during portfolio selection, then it is necessary to perform pairwise comparisons to calculate the relative preference of each criterion.

In the case of alternatives, assessment is based on each alternative (project) contribution to each criterion. Alternatives assessment is performed by specifying the forecasted value for each key measure if the project is implemented. If a project does not contribute to improve a particular KPI, then the current value of the KPI is used in the analysis. During project development, measures will remain unchanged, but once a project is completed, projects will deliver benefits and measures will be updated accordingly.

Figure 3. Benefit hierarchy template for project monitoring.

For the lower level of the hierarchy (the level of the alternatives) the evaluation considers numerical information updated at the control point of interest. In this way, KPIs’ values recorded in the BSC can be directly used and time-consuming pairwise comparisons are avoided.

3.2.2. Cost HierarchyThe approach to define the cost hierarchy is similar to the one used during portfolio selection. Criteria representing costs are derived from economic, environmental and social analysis developed for each project in the funded portfolio. The relative preference of each cost is performed using pairwise comparisons. Alternatives assessment is performed using raw data (see Figure 4).

4. Application CaseAlas Ingenieria is a small information technology company located in Bahía Blanca (Argentina) since 1991. The company provides advanced solutions for engineering and information management for industrial plants. They also provide support to develop, implement and integrate applications. Currently the company is organized under two segments –software products and services. The owner announced intent to explore options to promote growth, efficiency and improve the company´s social responsibility image. After performing a stakeholders’ analysis (whose description is out of the scope of this paper), a Strategy Map (see Figure 5) and a BSC are defined. In what follows, the results of project monitoring are described. The funded portfolio is composed of projects described in Table 1.

Figure 4. Cost hierarchy template for Project Monitoring.

4.1. Criteria and Sub-criteria DefinitionThe benefit hierarchy reflects the information provided by the BSC, i.e. perspectives; goals and KPIs (see Figure 6 and Figure 7). The cost impact categories which are particularly significant for this study are energy consumption, paper use and economic (initial costs and total cost of ownership).The cost hierarchy is structured using these categories (Figure 8).

4.2. Alternatives definitionAlternatives are given by projects included in Table 1. For each project, two alternatives are defined:

a) Alternative Pi, 1 ≤ i ≤ 18, represents a project as planned.

b) Alternative Ri, 1 ≤ i ≤ 18, represents the project´s status at a control point.




Figure 5. Strategy Map (partial view).

Table 1. Projects and supported goals in the Strategy Map.

Projects Goals in the Strategy MapProcess map definition 9 - 10 - 15 - 18Equipment repair and donation 13 - 23Train employees 12 - 16 - 17 - 25Process control 17ISO 9001:2008 certification 18Account information processing for financial analysis 1 - 18Cost analysis 1 - 18Train customers about responsible use of products 19Upgrade appliances and electronics 20Train employees about energy efficiency 20Paper less initiative 21Sustainable acquisition of products 22Responsibly disposal of batteries 23Paper recycling 23Develop employee discount programs 12 - 26Conduct employee performance evaluation 16Financial software module deployment 2CRM software module deployment 3 - 4 - 6 - 8


Sánchez, M. A.


Figure 6. Benefit hierarchy.

Figure 7. Benefit hierarchy (continuation).

Figure 8. Cost hierarchy.

4.3. Criteria, sub-criteria and alternatives definition

In this case, the same relative preference was given to all criteria. In this way, AHP and DEA results are derived from the same scenario. The Benefits and Costs Hierarchy show global and local priorities for each criteria and sub-criteria. For example, criteria Triple Bottom Line, Stakeholders, Internal Process and Learning and Growth have the same priority with respect to the global goal (25%). Alternatives assessment at control t point is performed using data updated to that instant of time.

4.4. Synthesis ResultsOnce that all judgments had been defined, numerical evaluations are computed using Expert Choice® software. Table 3 (Appendix I) includes results of benefits, costs, and an integrated score. The final score that integrates benefits and costs is calculated using expression (1) (see Section 3).

In order to give an interpretation to AHP priorities, recall that data used to assess alternatives Pi belong from project plans; while data used to assess alternative Ri at control point t (1 ≤ i ≤ 18), belong from data updated at this control point. Hence, the aim of AHP step is analyzing each pair Pi , Ri, and finding out if priorities are different. For example, if Pi score is greater than Ri score, then it may be that benefits have not been realized yet, or that Ri spending has been more than planned.

Table 4 (Appendix II) includes an interpretation of AHP synthesis. It can be seen that resulting priorities reflect the status of projects. The worst score is for project 5 because its costs are much higher than the rest of the projects. Alternative P1 has a low priority since forecasted cost is quite high. Projects 17 and 18 show a low priority because the score based on cost information is relatively bigger than others (see Table 3 in Appendix I).

4.5. DEA versus AHPThis section is devoted to compare and discuss AHP and DEA results. Since score numbers are not comparable in absolute terms, the aim is to find out if decisions derived from AHP results are consistent with decisions derived from AHP scores. In other words, for each pair Pi , Ri it is discussed if AHP scores are consistent with DEA ones.

Table 2 summarizes results and Table 5 (Appendix III) includes DEA scores. In general, they support the




same decisions based in AHP. It can be concluded that DEA scores are more precise with respect to the current progress of projects. DEA scores may not be realistic when the reference set of a project Ri is Pj, i ≠ j (see remarks about project 1 in Table 2).

Discrepancies in AHP and DEA rankings are not surprising since techniques are different. For example, DEA gives an outstanding rank for R5 (number 5). The score of P5 is smaller than R5´s score, and R5 is P5´s reference set. In other words, P5 is punished because it is compared to R5. In addition, there are no other units with similar production levels so R5 is deemed as efficient. This result arises because of the specialization problem that is a known drawback of DEA. By using AHP, the ranking of P5 (18) is

also worse than R5´s rank (17), but the difference is not so large. To summarize, while the comparison of AHP and DEA scores are consistent for pairs Pi ,Ri, rankings do not provide useful information because they compare all projects and management decisions should be based on the score analysis of pairs Pi ,Ri.

5. ConclusionsThis work describes how to use AHP as an aid in project management. The proposal is grounded on an existing project management framework to select and monitor projects based on DEA, and discusses modifications and additions arising from using AHP.

Table 2. Summary of AHP and DEA scores comparison for each pair Pi , Ri.

Projects AHP and DEA scores for Pi , Ri

Process map definition Even though DEA finds both P1 and R1 efficient, P1 score is better than R1. On the other hand, AHP computes a better score for R1. The current status is that almost all benefits have been realized and the spending is much less than planned. AHP results are consistent with this. DEA finds P7 as a reference set of R1, then R1 is not compared with P1.

Equipment repair and donation Both DEA and AHP give a better score to P2. However, DEA provides a bigger difference between P2 and R2. P2 is the reference set of R2. In fact, the total budget was spent and full benefits are expected in the future. DEA better highlights the situation.

Train employees Similar remarks as for project 2.Process control Similar remarks as for project 2.ISO 9001:2008 certification Consistent.Account information processing for financial analysis Consistent.Cost analysis Consistent.Train customers about responsible use of products Consistent.Upgrade appliances and electronics Consistent.Train employees about energy efficiency Consistent.Paper less initiative There is a slight difference between P11 and R11 AHP scores

while DEA scores are equal. However, it is doubtful that the decimal points are relevant. So it may be concluded that scores are consistent.

Sustainable acquisition of products Consistent.Responsibly disposal of batteries Consistent.Paper recycling Consistent.Develop employee discount programs DEA finds P15 inefficient and R15 efficient. AHP provides

the same score. The current scenario is that R15 provided more benefits than planned with the estimated budget. Then, DEA reflects the situation while AHP does not.

Conduct employee performance evaluation Consistent.Financial software module deployment Consistent.CRM software module deployment Consistent.


Sánchez, M. A.


The main components of the AHP model is a benefit hierarchy that structures goals represented in a BSC, and a cost hierarchy that includes costs derived from economic, environmental and social impacts derived from project development. Criteria and sub-criteria assessment is realized doing pairwise comparisons. For the case of alternatives, the evaluation considers raw data.

The use of AHP allows overcoming some limitations of DEA. The first is the introduction of preferences. The possibility of assigning preferences to criteria allows considering different scenarios and performing what-if analysis. Scenario analysis is often a requirement when selecting projects since uncertainty in many factors such market development, cost variance, among many others. In particular, when project selection takes into account economic, environmental and social dimensions, reasoning about the impact of each dimension enhances the analysis. The second limitation of DEA is the homogeneity assumption where all

DMUs are required to have comparable production levels. Since AHP does not require alternatives to be similar, projects of different size may be compared. In addition, AHP does not make assumptions about the number of alternatives. In DEA, it is desirable that the number of DMUs exceeds the number of inputs and outputs several times. Finally, AHP allows multiple decision makers to give judgments. While this option was not used in the work, it may be relevant since it favors inclusive approaches that allow the participation of multiple actors.

On the other hand, AHP has some disadvantages. Sometimes, it is not advisable to derive priorities directly from hard data because preferences are often not linearly related to data. For instance, if with respect to initial cost, alternative A is two times more preferable than B, then A may not be twice as preferable as B. How to systematically derive rating scales from raw data is a potential direction for research.

ReferencesAzis, I. (1990). Analytic Hierarchy Process in the Benefit-Cost Framework: A Post-Evaluation of the Trans-Sumatra Highway project. European

Journal of Operational Research, 48(1): 38-48. doi:10.1016/0377-2217(90)90059-K

Banker, R., Charnes, A., Cooper, W. (1984). Some models for estimating technical and scale inefficiencies in Data Envelopment Analysis. Management Science, 30(9): 1078-1092. doi:10.1287/mnsc.30.9.1078

Bible, M., Bivins, S. (2011). Mastering Project Portfolio Management. Fort Lauderdale: J. Ross Publishing, Inc.

Camanho, A., Dyson, R. (1999). Efficiency, size, benchmarks and targets for bank branches: an application of data envelopment analysis. Journal of the Operational Research Society, 50: 903-915. doi:10.1057/palgrave.jors.2600792

Chang, D., Yang, F. (2010). Data Envelopment Analysis with Two Distinct Objectives of Inputs or Outputs. In Proc. the 6th International Symposium on Management, Engineering and Informatics. International Institute of Informatics and Systemics, Florida, USA.

Charnes, A., Cooper, W., Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6): 429-444. doi:10.1016/0377-2217(78)90138-8

Cook, W., Seiford, L. (2009). Data Envelopment Analysis (DEA) -Thirty years on. European Journal of Operational Research, 192(1): 1-17. doi:10.1016/j.ejor.2008.01.032

Forman, E. S. (2001). Decision by Objectives - How to convince others that you are right. River Edge, New Jersey: World Scientific Publishing.

Joro, T., Viitala, E. (2004). Weight-restricted DEA in action: from expert opinions to mathematical models. Journal of the Operational Research Society, 55: 814-821. doi:10.1057/palgrave.jors.2601752

Kaplan, R., Norton, R. (2004). Strategy Maps. Converting Intangible Assets into Tangible Outcomes. Boston: Harvard Business School Press.

Kendrick, J., Saaty, D. (2007). Use Analytic Hierarchy Process for Project Selection. Six Sigma Forum Magazine, pp. 22-29.

Kumar, S. (2004). AHP- based formal system for R&D project evaluation. Journal of Scientific & Industrial Research, 63: 888-896.

Madlener, R., Antunes, C., Dias, L. (2009). Assessing the performance of biogas plants with multi-criteria and data envelopment analysis. European Journal of Operational Research, 197(3):1084-1094. doi:10.1016/j.ejor.2007.12.051

Mar, C. (2009). Specialization versus diversification: non-homogeneity in Data Envelopment Analysis. Proc. 3rd International Conference on Industrial Engineering and Industrial Management, Barcelona, Spain, pp. 1125-1133.

Porter, M., Kramer, M. (2011). Creating shared value. How to reinvent capitalism -and unleash a wave of innovation and growth. Harvard Business Review, 1-17.

Saaty, T. (1997). Toma de decisiones para líderes: El proceso analítico jerárquico. La toma de decisiones en un mundo complejo. Pittsburgh: RWS Publications.



http://dx.doi.org/10.1016/0377-2217(90)90059-K

http://dx.doi.org/10.1287/mnsc.30.9.1078

http://dx.doi.org/10.1057/palgrave.jors.2600792

http://dx.doi.org/10.1016/0377-2217(78)90138-8


http://dx.doi.org/10.1057/palgrave.jors.2601752



Saaty, T., Ozdemir, M. (2003). Negative priorities in the Analytic Hierarchy Process. Mathematical and Computer Modelling, 37(9-10): 1063-1075. doi:10.1016/S0895-7177(03)00118-3

Sánchez, M. (2014). Integrating Sustainability Issues into Project Management. Journal of Cleaner Production, 96: 319-330. doi:10.1016/j.jclepro.2013.12.087.

Shimshak, D., Lenard, M., Klimberg, R. (2009). Incorporating quality into data envelopment analysis of nursing home performance: a case study. Omega, 37(3): 672-685. doi:10.1016/j.omega.2008.05.004

Yalabik, B., Fairchild, R. (2011). Customer, regulatory, and competitive pressure as drivers of environmental innovation. International Journal of Production Economics, 131(2): 519-527. doi:10.1016/j.ijpe.2011.01.020

Appendix I. AHP Synthesis Results

Table 3. Detail of AHP results.

Projects (alternatives) Benefits Costs Integral ScoreP1: Process map definition 0.026 0.054 0.00048148P2: Equipment repair and donation 0.029 0.001 0.029P3: Train employees 0.028 0.013 0.00215385P4: Process control 0.028 0.001 0.028P5: ISO 9001:2008 certification 0.029 0.455 6.3736×10–5

P6: Account information processing for financial analysis 0.027 0.001 0.027P7: Cost analysis 0.028 0.001 0.028P8: Train customers about responsible use of products 0.028 0.053 0.0005283P9: Upgrade appliances and electronics 0.027 0.001 0.027P10: Train employees about energy efficiency 0.027 0.001 0.027P11: Paper less initiative 0.027 0.015 0.0018P12: Sustainable acquisition of products 0.029 0.001 0.029P13: Responsibly disposal of batteries 0.028 0.001 0.028P14: Paper recycling 0.027 0.001 0.027P15: Develop employee discount programs 0.029 0.018 0.00161111P16: Conduct employee performance evaluation 0.03 0.008 0.00375P17: Financial software module deployment 0.025 0.033 0.00075758P18: CRM software module deployment 0.03 0.03 0.001R1: Process map definition 0.027 0.023 0.00117391R2: Equipment repair and donation 0.028 0.001 0.028R3: Train employees 0.027 0.001 0.027R4: Process control 0.028 0.001 0.028R5: ISO 9001:2008 certification 0.028 0.18 0.00015556R6: Account information processing for financial analysis 0.027 0.001 0.027R7: Cost analysis 0.028 0.001 0.028R8: Train customers about responsible use of products 0.028 0.018 0.00155556R9: Upgrade appliances and electronics 0.027 0.001 0.027R10: Train employees about energy efficiency 0.027 0.001 0.027R11: Paper less initiative 0.027 0.013 0.00207692R12: Sustainable acquisition of products 0.028 0.001 0.028R13: Responsible disposal of batteries 0.028 0.001 0.028R14: Paper recycling 0.028 0.001 0.028R15: Develop employee discount programs 0.029 0.018 0.00161111R16: Conduct employee performance evaluation 0.028 0.008 0.0035R17: Financial software module deployment 0.025 0.033 0.00075758R18: CRM software module deployment 0.028 0.03 0.00093333


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http://dx.doi.org/10.1016/S0895-7177(03)00118-3

http://dx.doi.org/10.1016/j.jclepro.2013.12.087


http://dx.doi.org/10.1016/j.omega.2008.05.004

http://dx.doi.org/10.1016/j.ijpe.2011.01.020


Appendix II. Project Monitoring based on AHP

Table 4. Project Monitoring results based on AHP.

Projects Decision Alternative Score Comments

Process map definition ContinueP1 0.00048 In progress. Achievement of some benefits.

Costs less than planned.R1 0.0018

Equipment repair and donation Continue

P2 0.029 Full benefits expected on the final period. Total budget spent.R2 0.028

Train employees ContinueP3 0.002154 In progress. Some benefits expected on the final

period, others benefits expected in the long-term. Costs less than planned.R3 0.027

Process control ContinueP4 0.028 In progress. Some benefits expected on the final

period, others benefits expected in the long-term. Costs less than planned.R4 0.028

ISO 9001:2008 certification Continue

P5 6.37363×10–5 In progress. Achievement of some benefits. Costs less than planned.R5 0.00016

Account information processing for financial analysis

CompletedP6 0.027

Deliverables fully accomplished. On budget.R6 0.027

Cost analysis CompletedP7 0.028

Deliverables fully accomplished. On budget.R7 0.028

Train customers about responsible use of products

ContinueP8 0.00053 In progress. Achievement of some benefits.


Upgrade appliances and electronics Completed

P9 0.027Deliverables fully accomplished. On budget.

R9 0.027

Train employees about energy efficiency Completed


R10 0.027

Paper less initiative ContinueP11 0.0018 In progress. Achievement of some benefits.


Sustainable acquisition of products Continue

P12 0.029 In progress. Achievement of some benefits. Costs less than planned.R12 0.028

Responsible disposal of batteries Continue

P13 0.028 In progress. Achievement of some benefits. Costs less than planned.R13 0.028

Paper recycling ContinueP14 0.027 In progress. Achievement of all benefits. Costs

less than planned.R14 0.028

Develop employee discount programs Continue

P15 0.0016 In progress. Achievement of some benefits. On budget.R15 0.0016

Conduct employee performance evaluation Continue


Financial software module deployment Completed


R17 0.00076

CRM software module deployment Continue





Appendix III. Project Monitoring based on DEA

Table 5. Project Monitoring scores based on DEA.

DMU Score DMU Score DMU ScoreP1 1.03 P7 1.00 P13 1.02

R1 1.00 R7 1.00 R13 1.03

P2 1.01 P8 1.00 P14 1.00

R2 0.38 R8 1.02 R14 1.03

P3 1.06 P9 0.77 P15 0.76

R3 1.00 R9 0.77 R15 1.02

P4 1.02 P10 1.00 P16 1.02

R4 0.35 R10 1.00 R16 0.82

P5 1.01 P11 0.76 P17 0.17R5 1.03 R11 0.76 R17 0.17P6 1.00 P12 1.01 P18 1.05

R6 1.00 R12 1.02 R18 0.31


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PME

IJ





A robust evaluation of sustainability initiatives with analytic network process (ANP)

Lanndon Ocampoa and Christine Omela Ocampob

a Department of Mechanical Engineering, University of San Carlos, Cebu City, 6000 Cebu, Philippines [email protected]

b Department of Industrial Engineering, University of San Carlos, Cebu City, 6000 Cebu, Philippines [email protected]

Abstract: This paper presents a methodology on evaluating sustainable manufacturing initiatives using analytic network process (ANP) as its base. The evaluation method is anchored on the comprehensive sustainable manufacturing framework proposed recently in literature. A numerical example that involves an evaluation of five sustainable manufacturing initiatives is shown in this work. Results show that sustainable manufacturing implies enhancing customer and community well-being by means of addressing environmental issues related to pollution due to toxic substances, greenhouse gas emissions and air emissions. To test the robustness of the results, two approaches are introduced in this work: (1) using Monte Carlo simulation and (2) introducing structural changes on the evaluation model. It suggests that the results are robust to random variations and to marginal changes of the network structure. The contribution of this work lies on presenting a sustainable manufacturing evaluation approach that addresses complexity and robustness in decision-making.

Key words: Analytic Network Process, Evaluation, Manufacturing, Robustness, Sustainability.

1. IntroductionIn sustaining manufacturing industry, purely profit-based strategies became insufficient brought about by various issues that concern environmental degradation, resource depletion, carbon emissions, and social responsibility. These issues are associated with the interests of various stakeholders who are capable of influencing salient decisions of manufacturing firms (Pham and Thomas, 2012). These stakeholders, which include customers, employees, investors, suppliers, communities and governments (Theyel and Hofmann, 2012) directly or indirectly compel manufacturing firms to manage the performance of their products and processes in order to satisfy persistent issues on resource depletion, socio-economic concerns and human health problems. When these demands from stakeholders are integrated in mainstream decision-making, manufacturing firms could establish long term relations with these stakeholders (Harrison et al., 2010). This is believed to be beneficial from the perspective of the manufacturing industry as

stakeholders play a crucial role in the sustainability of manufacturing firms (Kassinis and Vafeas, 2006; Paloviita and Luoma-aho, 2010).

Ocampo and Clark (2014a) implied that these demands from stakeholders are pushing firms to gear up towards a more holistic concept of the triple-bottom-line – a term first coined by Elkington (1997) – which interprets sustainability into three main dimensions: environmental stewardship, economic growth and social well-being. Labuschagne et al. (2005) claimed that optimal approaches of manufacturing firms towards sustainability are only possible when these three dimensions are taken into consideration. From the perspective of sustainability of manufacturing firm emerges specialized framework popularly known as ‘sustainable manufacturing’ and is defined as the “creation of manufactured products that use processes that minimize negative environmental impact, conserve energy and natural resources, are safe for employees, communities and consumers, and are economically sound” (International Trade Administration, 2007). Operationally, manufacturing firms must:




(1) design and manufacture eco-efficient products with processes that possess minimal environmental footprint using a life cyclce assessment (LCA) approach, (2) develop initiatives on cost reduction and return on investment maximization across organizational levels, and (3) maintain programs that enhance well-being of stakeholders (Ocampo, 2015). Recent studies claim that firms that promote sustainability in their decision-making are more likely to be successful in their respective industries (Jayal et al., 2010).

Among various research domains in this area, evaluation of manufacturing initiatives that promote sustainability is popularly taken (Joung et al., 2013; Ocampo and Clark, 2015). The basis of evaluation is usually anchored on some established indicator sets (Jayal et al., 2010; Ocampo and Clark, 2014b; Ocampo and Clark, 2015). These indicator sets provide verifiable standards in evaluating products, processes, firm, economic sectors or even countries and regions in the context of sustainable manufacturing (Joung et al., 2013). A review of these indicator sets were discussed in Mayer (2008), Joung et al. (2013), Ocampo and Clark (2014b), Ocampo and Clark (2015), Ocampo (2015) and will not be repeated here. The challenge of these indicators sets is twofold: (1) being comprehensive, and (2) being operational. A plausible integration of these indicators sets that attempts to cover sustainability areas in great detail was proposed by Joung et al. (2013) and this framework was used by Ocampo and Clark (2014b), Ocampo and Clark (2015) and Ocampo (2015).

Ocampo (2015) utilized the framework of Joung et al. (2013) in index computation to assess sustainability of manufacturing at firm level. Ocampo and Clark (2015) used the same structure to evaluate sustainable manufacturing of a case firm using analytic hierarchy process (AHP). Ocampo and Clark (2014b) extended the former evaluation to include causal relationships between criteria and across the decision model using the general analytic network process (ANP). Despite of these recent works, the specific problem that is advanced in this paper is an evaluation framework that captures complexity and robustness of decision-making in the framework of sustainable manufacturing.

This paper extends previous works by embedding robustness in sustainable manufacturing evaluation in the context of the ANP. Following the argument of Ocampo and Clark (2014b) on the use of ANP, this work imposes such use due to the complexity and multi-dimensionality of the evaluation problem

associated with the issues that concern sustainability. Developed by Thomas Saaty, ANP generalizes any decision-making problem by overcoming the hierarchic assumption mostly characterized by other decision-making tools (Saaty, 2001). The use of ANP in sustainable manufacturing evaluation allows comprehensiveness of addressing the complexity inherent in the decision-making process. Chen et al. (2012) agreed that AHP and ANP are appropriate analytical tools for addressing location, program or strategy selection problems. Among various applications that highlight the use of ANP include developing sustainability index for a manufacturing enterprise (Garbie, 2011), developing multi-actor multi-criteria approach in complex sustainability project evaluation (de Brucker et al., 2013), evaluating industrial competitiveness (Sirikrai and Tang, 2006), evaluating energy sources (Chatzimouratidis and Pilavachi, 2009), developing an impact matrix and sustainability-cost benefit analysis (Chiacchio, 2011), etc. The departure of this work include: (1) evaluating robustness of the results of the evaluation problem and, (2) determining the impact of structural changes of the evaluation problem on the results of the ANP. The contribution of this work is on presenting a sustainable manufacturing evaluation approach that addresses complexity and robustness in decision-making.

This paper is organized as follows: Section 2 presents the methodology of the study. Section 3 highlights the evaluation model along with the results of the ANP and robustness tests. Section 4 provides the discussion and ends with concluding remarks in Section 5.

2. MethodologyThe proposed evaluation approach can be generally described in the following procedure:

1. Incorporate feedback and dependence relationships on the hierarchical sustainable manufacturing evaluation framework proposed by Ocampo and Clark (2015). This is presented in the parallel work of Ocampo and Clark (2014b). The ten sustainable manufacturing initiatives under evaluation were described in the concept paper of Ocampo and Clark (2014a). Although, they attempt to develop an evaluation method following the demands of stakeholders and the triple-bottom line, the approach was not generalizable (Ocampo and Clark, 2014a). By convention, an arrow that emanates from one component to another component implies


Ocampo, L and Ocampo, C. O.

that the latter influences the former. Introducing these dependence relationships is based from theory and practice of sustainability as discussed by Ocampo and Clark (2014a).

2. Based from the resulting network of step 1, corresponding pairwise comparisons matrices are constructed. A detailed discussion on this topic was provided by Saaty (2001). In eliciting pairwise comparisons, generally we ask this question: “Given a control element, a component (element) of a given network, and given a pair of component (or element), how much more does a given member of the pair dominate other member of the pair with respect to a control element?” (Promentilla, et al., 2006). Saaty’s Fundamental Scale (Saaty, 1980), as shown in Table 1, is used to compare elements pairwise. Note that a pairwise comparisons matrix possesses a reciprocal characteristic, i.e.

1a ji aij= .

Table 1. Saaty fundamental scale (adopted from Saaty, 1980).

Definition Explanation

1 Equal importance Two elements contribute equally to the objective

2 Weak between equal and moderate

3Moderate importance Experience and judgment

slightly favor one element over another

4 Moderate plus between moderate and strong

5Strong importance Experience and judgment

strongly favor one element over another

6 Strong plus between strong and very strong

7

Very strong or demonstrated importance

An element is favored very strongly over another; its dominance demonstrated in practice

8 Very, very strong between very strong and extreme

9

Extreme importance The evidence favoring one element over another is one of the highest possible order or affirmation

Determining the priority vector of a pairwise comparisons matrix involves solving an eigenvalue problem in the form

Aw=λmaxw (1)

where A is the positive reciprocal of the pairwise comparisons matrix and w is the principal eigenvector associated with the maximum eigenvalue λmax. Saaty (1980) claimed that w is the best estimate of the priority vector of the pairwise comparisons matrix.

For consistent judgment, λmax=n; otherwise, λmax>n where n is the number of elements being compared. Consistency of judgment is measured using consistency index (CI) and consistency ratio (CR). CI is a measure of the degree of consistency of judgment and is denoted by

CI=λmax–nn–1 (2)

CR is computed as

CICRRI

= (3)

where RI is the mean random consistency index. CR≤0.10 is an acceptable degree of consistency (Saaty, 1980). Otherwise, decision-makers will be asked to reconsider their judgments.

3. Form the initial supermatrix based from the network developed in step 1. See Saaty (1980) on the discussion of supermatrix. Populate this initial supermatrix with the local priority vectors obtained in step 2. Then, transform the initial supermatrix to column stochastic supermatrix by normalizing column values such that column sum is unity. Finally, raise the stochastic supermatrix to sufficiently large powers until row values become identical. Each column of this limiting supermatrix is likewise identical and is known as the global eigenvector of the supermatrix. This is used to describe the overall dominance of the elements in the decision network.

4. To test the robustness of the results, this paper adopted two approaches. First, Monte Carlo simulation was performed to determine the effect of repeated decisions on the final ranking of results. Second, structural changes of the decision network were introduced to evaluate their impact on the final ranking. Comparison of the results with the findings of Ocampo and Clark (2014b) and Ocampo and Clark (2015) were reported.

3. ResultsThe evaluation problem proposed by Ocampo and Clark (2015) was based from the hierarchical




sustainability indicators set proposed by Joung et al. (2013) along with the sustainable manufacturing initiatives discussed by Ocampo and Clark (2014b). This problem is composed of the goal, the triple-bottom line (environmental stewardship, economic growth and social well-being), 10 sub-criteria, 33 attributes and 5 sustainability initiatives. Using analytic hierarchy process (AHP), the work was able to assign priority ranking of sustainability initiatives that the case firm must adopt to further promote sustainability.

Although the dependence relationships were shown in Ocampo and Clark (2014b), the motivations behind these relationships are discussed in this paper. In this work, the hierarchical structure of Joung et al. (2013) was still used while feedback and dependence relations in the criteria and sub-criteria components were introduced. This approach of introducing feedback and dependence relationships in the criteria and sub-criteria components, excluding the attribute component, was done to provide interrelationships at an intermediate level while maintaining hierarchical dependence at lower level. This allows control from upper level decision components to the lower level

components. Figure 1 shows the evaluation problem and Table 2 presents the decision components and elements along with their corresponding codes. The details of this coding system were discussed by Ocampo and Clark (2014b).

As shown in Figure 1, attribute component contains no dependence relationships as they only become redundant due to the existing relationships in higher level components. The hierarchical dependence relationships from goal – criteria – sub-criteria – attributes were based from the work of Ocampo and Clark (2015). Note that all decision components have feedback control loop towards the goal component. This is a structural issue as it guarantees that the the goal component takes control over all other components in the evaluation problem.

In this paper, pairwise comparisons matrices of the hierarchical dependence relationships from goal – criteria – sub-criteria – attributes were obtained from Ocampo and Clark (2015). Generally, there are three sets or levels of pairwise comparisons matrices performed in this work. First is the dependence relationships among elements in the criteria component and Table 3 shows a sample of these

Ocampo, L. & Ocampo, C.O.

Creative Commons Attribution-NonCommercial 4.0 Int. J. Prod. Manag. Eng. (2015) 3(2), ppp-ppp | 3

International License

Figure 1. Decision network of the evaluation of sustainable manufacturing initiatives

Criteria

G

A B C

Sub criteria

A1 A2 A3 A4 B3B1 B2 C1 C3C2

Initiatives

I1 I2 I4 I5I3

Attributes

A1

A1

A1

A1

A1

A2

A2

A2

A2

A4

A4

A4

A3

A3

A3

A3

B1

B1

B2

B2

B2

B2

B3

B3

C1

C1

C1

C2

C2

C2

C3

C3

C3

Figure 1. Decision network of the evaluation of sustainable manufacturing initiatives.



pairwise comparisons matrices. The question being asked in Table 3 is: “Comparing environmental dimension (A) and economic dimension (B), which one more dominates environmental dimension (A) and by how much?” The resulting priority vector is reported using equation (1). Second is the dependence relationships among elements in the sub-criteria component and Table 4 shows a sample of these pairwise comparisons matrices. The question being asked in Table 4 is: “Comparing pollution (A1) and emission (A2), which one more influences the community (C3) and by how much?” The resulting priority vector is again reported. Lastly, pairwise comparisons were performed on the hierarchical dependence relationships of sub-criteria to sustainable manufacturing initiatives. Table 5 shows a sample of these pairwise comparisons matrices. The question being asked in Table 5 is: “Comparing health and wellness program (I1) and employee compensation and benefits (I2), which one more characterizes toxic substance (A11) and by how much?” The resulting priority vector is reported.

The supermatrix in Table 6 is populated by the priority vectors provided by Ocampo and Clark

(2015) on hierarchical dependence relationships of the network model and the resulting vectors obtained in this work. To facilitate discussion, let A, B, C, D and E be the goal, criteria, sub criteria, attributes and initiatives decision components. Generally, based from the network presented in Fig. 1, the supermatrix can be structured as in Table 6.

Table 3. Pairwise comparisons of the dominance of criteria with respect to environmental criterion (A).

A A B C Priority vectorA 1 3 2 0.5396B 1/3 1 1/2 0.1634C 1/2 2 1 0.2970

λmax=3.009, CR=0.009

Table 4. Pairwise comparisons of the dominance of sub-criteria with respect to community (C3).C3 A1 A2 A3 A4 Priority vectorA1 1 2 4 3 0.4673A2 1/2 1 3 2 0.2772A3 1/4 1/3 1 1/2 0.0954A4 1/3 1/2 2 1 0.1601

λmax=4.031, CR=0.012

Table 2. Decision elements and their codes (adopted from Ocampo and Clark, 2015).

Decision components and elements

Code Decision components and elements

Code Decision components and elements

Code

Evaluation of sustainable manufacturing

G Effluent A21 Employees health and safety C11

Environmental stewardship A Air emissions A22 Employees career development C12Economic growth B Solid waste emissions A23 Employee satisfaction C13Social well-being C Waste energy emissions A24 Health and safety impacts from

manufacturing and product useC21

Pollution A1 Water consumption A31 Customer satisfaction from operations and products

C22

Emissions A2 Material consumption A32 Inclusion of specific rights to customer

C23

Resource consumption A3 Energy/electrical consumption A33 Product responsibility C31Natural habitat conservation A4 Land use A34 Justice/equity C32Profit B1 Biodiversity management A41 Community development

programsC33

Cost B2 Natural habitat quality A42 Health and wellness program I1Investment B3 Habitat management A43 Employee compensation and

career developmentI2

Employee C1 Revenue B11 Occupational health and safety I3Customer C2 Profit B12 Elimination of lead in plating

processI4

Community C3 Materials acquisition B21 Lean six sigma initiatives I5Toxic substance A11 Production B22Greenhouse gas emissions A12 Product transfer to customer B23Ozone depletion gas emissions

A13 End-of-service-life product handling

B24

Noise A14 Research and development B31Acidification substance A15 Community development B32




Table 5. Pairwise comparisons of the dominance of sustainable manufacturing initiatives with respect toxic substance (A11).A11 I2 I3 I4 I5 I9 Priority vectorI2 1 4 2 1/2 4 0.2697I3 1/4 1 1/3 1/5 1 0.0682I4 1/2 3 1 1/3 3 0.1688I5 2 5 3 1 5 0.4252I9 1/4 1 1/3 1/5 1 0.0682

λmax=5.062, CR=0.014

The supermatrix in Table 6 is populated by the priority vectors provided by Ocampo and Clark (2015) on hierarchical dependence relationships of the network model and the resulting vectors obtained from this work.

Table 6. Blocks of the supermatrix.A B C D E

A 1 1 1 1 1B BA BB 0 0 0C 0 diag [CB] CC 0 0D 0 0 diag [DC] I 0E 0 0 0 DC I

Note that the first row in the supermatrix which is composed of blocks AA, AB, AC, AD, and AE is a unity vector. This is the representation of the feedback control loop from components to the goal element. Block BA, i.e. B dominates A, is a hierarchical dependence relation from goal to criteria component. Blocks CB and DC are diagonal matrices resulting from dominance relationships of lower level elements to their parent criteria. CB denotes dominance relations of sub-criteria component to their parent criteria element while DC is the dominance of attributes to their parent sub-criteria. Blocks BB and CC denote interdependencies in the criteria and sub-criteria component, respectively. Block DC is a hierarchical dependence relation of attribute component to sustainable manufacturing initiatives. Identity matrices represented by blocks DD and EE show inner dependence relationships of the elements in the attributes and initiatives components, respectively. Null matrices for the rest of the blocks in the supermatrix represent non-existent feedback and dependence relationships on the elements of decision components. The initial supermatrix is presented in Appendix 1. A stochastic matrix is formed by dividing column values of the initial supermatrix with their corresponding column sums. Then, the stochastic matrix is raised to large powers until it converges to its Cesaro sum. Convergence exists if row values are identical. Each column is the global priority vector and is used to measure the overall dominance of each element in the supermatrix. Priority ranking of elements was performed per decision component. This was

obtained by normalizing values per component. Table 7 shows the ranking of the elements per component.

Table 7. Priority ranking of decision elements.

Elements Raw vectorDistributive

rankingIdeal

ranking RankG 0.39578 1 1 1A 0.06823 0.22986 0.59151 3B 0.11535 0.38861 1 1C 0.11325 0.38153 0.98180 2A1 0.01920 0.10449 0.59689 5A2 0.02279 0.12408 0.70875 3A3 0.01337 0.07278 0.41571 8A4 0.00428 0.02330 0.13308 10B1 0.01758 0.09568 0.54653 6B2 0.02279 0.12406 0.70864 4B3 0.02454 0.13358 0.76305 2C1 0.01447 0.07875 0.44986 7C2 0.03216 0.17506 1 1C3 0.01253 0.06822 0.38967 9A11 0.00334 0.04495 0.40775 6A12 0.00334 0.04495 0.40775 6A13 0.00115 0.01554 0.14097 22A14 0.00062 0.00837 0.07592 31A15 0.00115 0.01554 0.14097 22A21 0.00263 0.03544 0.32152 10A22 0.00526 0.07089 0.64305 2A23 0.00263 0.03544 0.32152 10A24 0.00088 0.01181 0.10717 27A31 0.00201 0.02702 0.24516 15A32 0.00067 0.00901 0.08172 30A33 0.00201 0.02702 0.24516 15A34 0.00201 0.02702 0.24516 15A41 0.00107 0.01442 0.13080 26A42 0.00053 0.00721 0.06540 32A43 0.00053 0.00721 0.06540 32B11 0.00330 0.04441 0.40289 8B12 0.00330 0.04441 0.40289 8B21 0.00228 0.03071 0.27861 13B22 0.00228 0.03071 0.27861 14B23 0.00114 0.01536 0.13930 24B24 0.00114 0.01536 0.13930 24B31 0.00409 0.05512 0.50000 3B32 0.00818 0.11023 1 1C11 0.00260 0.03509 0.3184 12C12 0.00087 0.01170 0.1061 28C13 0.00087 0.01170 0.1061 28C21 0.00193 0.02600 0.2359 18C22 0.00386 0.05201 0.4718 5C23 0.00386 0.05201 0.4718 4C31 0.00157 0.02111 0.1915 19C32 0.00157 0.02111 0.1915 21C33 0.00157 0.02111 0.1915 19I1 0.00876 0.17697 0.5898 3I2 0.00701 0.14160 0.4719 5I3 0.00837 0.16919 0.5639 4I4 0.01484 0.30004 1 1I5 0.01050 0.21220 0.70726 2



In order to test the robustness of these results, two approaches were performed. First, a Monte Carlo simulation of 500 runs is used to show the impact of randomness on the final results. This is done in a POM for Windows application software which is available in public domain. Second, structural revisions of the decision network were introduced to assess the impact of dependence relationships on the ANP results. In this approach, interdepence relationships of the sub-criteria component were eliminated and then results were subsequently reported. Furthermore, all interdependence relationships of criteria and sub-criteria components were removed and results were reported.

Table 8 summarizes the Monte Carlo simulation results. It shows that the ANP order ranking of I4-I5-I1-I3-I2 in decreasing priority is fairly robust after 500 random simulation runs which yield the order ranking of I4-I5-I1-I2-I3 in decreasing priority with rank reversal in the last two initiatives.

Table 8. Comparison with Monte Carlo simulation results.

Sustainable manufacturing initiatives

ANP resultsMonte Carlo simulation

Priority Rank Priority RankI1 0.18 3 0.16 3I2 0.14 5 0.15 4I3 0.17 4 0.12 5I4 0.30 1 0.29 1I5 0.21 2 0.28 2

Table 9 presents a comparison of ANP results with the results from structural changes. It shows that the absence of interdependencies in the sub-criteria component changes the ranking of I1 and I3. On the other hand, the complete absence of interdependencies in the decision network changes the top priority, i.e. I5 instead of I4.

Table 9. Impact of structural changes in the decision network.

ANP results

Absence of sub-criteria

interdepencies

Complete absence of

interdependenciesPriority Rank Priority Rank Priority Rank

I1 0.18 3 0.17 4 0.17 4I2 0.14 5 0.14 5 0.15 5I3 0.17 4 0.18 3 0.18 3I4 0.30 1 0.26 1 0.25 2I5 0.21 2 0.25 2 0.26 1

Finally, the results of this paper were compared with the results of Ocampo and Clark (2014b) and Ocampo and Clark (2015). Table 10 highlights the comparison.

Table 10. Comparison of the results.

Current results with Monte Carlo simulation

Ocampo and Clark (2015)

with AHP

Ocampo and Clark (2014b)

with ANPRank Rank Rank

I1 3 4 3I2 4 5 5I3 5 3 4I4 1 2 1I5 2 1 2

Table 10 shows that the results of the methodology are not consistent with the results of Ocampo and Clark (2015) but are fairly consistent with Ocampo and Clark (2014b).

4. DiscussionValuable insights could be gained from the results of this paper. ANP provides insightful approach in better understanding the evaluation of sustainable manufacturing initiatives. In the criteria component, economic dimension (B) is preferred over social dimension (C) which ranks second and environmental dimension (A) which ranks third. This ranking supports the results of Ocampo and Clark (2015) with minor differences on the priority weights. Economic and social dimensions have almost equal weights which means that manufacturing firms must focus on economic gains and their corresponding social impacts, i.e. welfare of stakeholders which may include employees, customers and community. Addressing social issues as results of economic decisions could be achieved via environmental impact on manufactured products and manufacturing processes. This claim is supported by the ranking in the sub-criteria component. Customer (C2), investment (B3), emissions (A2), cost (B2), and pollution (A1) are sub-criteria on top priority. The details of this ranking could be examined by taking a look at the priority attributes in the lower level decision component. Community development (B32), air emissions (A22), investment to research and development (B31), inclusion of customer rights (C23), customer satisfaction (C22), toxic substance (A11), and GHG emissions (A12) are on top priority in the attribute component. Thus, manufacturing decision-making must focus on maximizing revenue and profit by maximizing investment on research and development in technology and investment that contributes community development. Investments on community development implies developing




and implementing initiatives that minimize environmental impact of toxic substance, GHG and air emissions. Revenue and profit are maximized by reinforcing customer satisfaction strategies and by inclusion of customer rights on manufactured products. Developing initiatives that simultaneously enhance customer satisfaction and community development by addressing environmental concerns on toxic substance, GHG emissions and air emissions is fundamentally important to increase revenue and profit. This ranking influences the priority ranking of sustainable manufacturing initiatives. The rank is as follows: elimination of lead in plating process (I4), lean six sigma initiatives (I5), health and wellness program (I1), occupational health and safety (I3) and employee compensation and career development (I2). The first initiative, which is a cleaner production technology, is developed to satisfy customer requirements and at the same time promotes community development through embedding decreased risks associated with occupational sarety and health. Cleaner production in a wider scale could promote greater social welfare as the society becomes a direct stakeholder on the environmental issues related to manufactured products and manufacturing process.

These results differ marginally with the results of Ocampo and Clark (2015) using AHP of the same research problem. Their results provide less emphasis on environmental impact and greater emphasis on minimizing costs due to the pure independence assumption in the criteria component. When feedback and dependence are taken into account, environmental issues must be addressed to enhance social impact which is vital for sustainability. Future research must direct how to develop strategies in designing products and processes that will provide long term benefits to the customer and to the community as well.

These results were subjected to test of robustness using Monte Carlo simulation that attempts to repeat the results over several simulation runs, i.e. 500 runs in this study. Results show that these ANP results are fairly robust with the exception in the bottom two initiatives. It implies that this priority ranking is dependable and the case firm could use this as an

input in prioritizatizing investments, for instance. The absence of interdependence relationships among sub-criteria could also change the ranking except for the first two initiatives. This indicates that the first two decisions are robust enough such that minor changes in the decision model could hardly change their priority ranking. Lastly, it is interesting to note that the ranking with complete absence of interdependencies are consistent with the results of Ocampo and Clark (2015) using AHP. This is due to the inherent structure of the decision network. When interdependencies are removed, the decision network approaches the structure of a hierarchy such that the appropriate methodology becomes the AHP.

5. ConclusionThis paper demonstrates the use of analytic network process (ANP) in evaluating sustainable manufacturing initiatives. The decision problem is structured as a hierarchical network which is built upon the model of Ocampo and Clark (2014b) and Ocampo and Clark (2015). Results show that cleaner production technologies, i.e. elimination of lead in the plating process, are considered on topmost priority. This work suggests that sustainable manufacturing is achieved by formulating strategies that address issues on customer and community well-being by means of focusing on environmental concerns, e.g. toxic substance, GHG emissions and air emissions. To test the robustness of these results, this work adopts two approaches: (1) using Monte Carlo simulation, (2) introducing structural changes on the evaluation model. Results show that the first two topmost sustainable manufacturing initiatives are robust enough for the case firm to subscribe in these results. Future work must focus on formulating specific policies regarding the design of products and processes that could enhance customer and community welfare.

Acknowledgements

We are grateful with the insightful comments from two anonymous reviewers that helped us improve the quality of this paper.

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Appendix 1. Initial supermatrixG A B C A1 A2 A3 A4 B1 B2 B3 C1 C2 C3 A11 A12 A13 A14 A15 A21 A22 A23 A24 A31 A32 A33 A34 A41 A42 A43 B11 B12 B21 B22 B23 B24 B31 B32 C11 C12 C13 C21 C22 C23 C31 C32 C33 I1 I2 I3 I4 I5

G 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

A 0.2000 0.5396 0.2297 0.2000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0B 0.4000 0.1634 0.6483 0.2000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

C 0.4000 0.2970 0.1220 0.6000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

A1 0 0.3511 0 0 0.667 0 0 0.1601 0 0 0 0.3333 0.6667 0.4673 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0A2 0 0.3511 0 0 0.333 1 0 0.095 0 0 0 0.6667 0.3333 0.277 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0A3 0 0.1609 0 0 0 0 1 0.277 0 1 0 0 0 0.095 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

A4 0 0.1368 0 0 0 0 0 0.467 0 0 0 0 0 0.16 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

B1 0 0 0.4000 0 0 0 0 0 0.5000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0B2 0 0 0.4000 0 0 0 0 0 0.2500 0.7500 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

B3 0 0 0.2000 0 0 0 0 0 0.2500 0.2500 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

C1 0 0 0 0.2500 0 0 0 0 0 0.297 0 1 0 0.2500 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0C2 0 0 0 0.5000 0 0 0 0 1 0.54 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

C3 0 0 0 0.2500 0 0 0 0 0 0.163 0 0 0 0.7500 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

A11 0 0 0 0 0.3475 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0A12 0 0 0 0 0.3475 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0A13 0 0 0 0 0.1201 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0A14 0 0 0 0 0.0647 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

A15 0 0 0 0 0.1201 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

A21 0 0 0 0 0 0.2308 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0A22 0 0 0 0 0 0.4615 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0A23 0 0 0 0 0 0.2308 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

A24 0 0 0 0 0 0.0769 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

A31 0 0 0 0 0 0 0.3000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0A32 0 0 0 0 0 0 0.1000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0A33 0 0 0 0 0 0 0.3000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

A34 0 0 0 0 0 0 0.3000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

A41 0 0 0 0 0 0 0 0.5000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0A42 0 0 0 0 0 0 0 0.2500 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

A43 0 0 0 0 0 0 0 0.2500 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

B11 0 0 0 0 0 0 0 0 0.5000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

B12 0 0 0 0 0 0 0 0 0.5000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

B21 0 0 0 0 0 0 0 0 0 0.3333 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0B22 0 0 0 0 0 0 0 0 0 0.3333 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0B23 0 0 0 0 0 0 0 0 0 0.1667 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

B24 0 0 0 0 0 0 0 0 0 0.1667 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

B31 0 0 0 0 0 0 0 0 0 0 0.3333 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

B32 0 0 0 0 0 0 0 0 0 0 0.6667 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0

C11 0 0 0 0 0 0 0 0 0 0 0 0.6000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0C12 0 0 0 0 0 0 0 0 0 0 0 0.2000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0

C13 0 0 0 0 0 0 0 0 0 0 0 0.2000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0

C21 0 0 0 0 0 0 0 0 0 0 0 0 0.2000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0C22 0 0 0 0 0 0 0 0 0 0 0 0 0.4000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0

C23 0 0 0 0 0 0 0 0 0 0 0 0 0.4000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0

C31 0 0 0 0 0 0 0 0 0 0 0 0 0 0.3333 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0C32 0 0 0 0 0 0 0 0 0 0 0 0 0 0.3333 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0

C33 0 0 0 0 0 0 0 0 0 0 0 0 0 0.3333 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0

I1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.2697 0.2000 0.2156 0.2264 0.2889 0.1869 0.1750 0.1250 0.1237 0.1084 0.1084 0.1237 0.1667 0.1429 0.1429 0.1429 0.1199 0.0450 0.1237 0.0655 0.1667 0.2000 0.0780 0.3309 0.3325 0.1367 0.1367 0.1429 0.1237 0.1667 0.1237 0.2000 0.2500 1 0 0 0 0I2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0682 0.0780 0.0801 0.0558 0.0727 0.0534 0.0636 0.1250 0.1237 0.1084 0.1084 0.1237 0.1667 0.1429 0.1429 0.1429 0.1199 0.0670 0.1237 0.0655 0.1667 0.2000 0.0780 0.3309 0.0896 0.4030 0.4030 0.1429 0.1237 0.1667 0.1237 0.2000 0.2500 0 1 0 0 0I3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.1688 0.1255 0.1293 0.4904 0.1228 0.1213 0.1077 0.1250 0.1237 0.1084 0.1084 0.2343 0.1667 0.1429 0.1429 0.1429 0.2101 0.2191 0.1237 0.2500 0.1667 0.2000 0.1342 0.1985 0.3325 0.1367 0.1367 0.1429 0.1237 0.1667 0.1237 0.2000 0.2500 0 0 1 0 0I4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.4252 0.5459 0.5231 0.0867 0.4429 0.5871 0.5900 0.5000 0.2343 0.4737 0.2011 0.1237 0.3333 0.4286 0.4286 0.4286 0.0706 0.1480 0.2343 0.1094 0.1667 0.2000 0.4882 0.0844 0.2012 0.0791 0.0791 0.4286 0.2343 0.1667 0.3945 0.2000 0.1250 0 0 0 1 0

I5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0682 0.0507 0.0518 0.1407 0.0727 0.0513 0.0636 0.1250 0.3945 0.2011 0.4737 0.3945 0.1667 0.1429 0.1429 0.1429 0.4795 0.5210 0.3945 0.5096 0.3333 0.2000 0.2215 0.0553 0.0443 0.2444 0.2444 0.1429 0.3945 0.3333 0.2343 0.2000 0.1250 0 0 0 0 1



G A B C A1 A2 A3 A4 B1 B2 B3 C1 C2 C3 A11 A12 A13 A14 A15 A21 A22 A23 A24 A31 A32 A33 A34 A41 A42 A43 B11 B12 B21 B22 B23 B24 B31 B32 C11 C12 C13 C21 C22 C23 C31 C32 C33 I1 I2 I3 I4 I5

G 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

A 0.2000 0.5396 0.2297 0.2000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0B 0.4000 0.1634 0.6483 0.2000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

C 0.4000 0.2970 0.1220 0.6000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

A1 0 0.3511 0 0 0.667 0 0 0.1601 0 0 0 0.3333 0.6667 0.4673 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0A2 0 0.3511 0 0 0.333 1 0 0.095 0 0 0 0.6667 0.3333 0.277 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0A3 0 0.1609 0 0 0 0 1 0.277 0 1 0 0 0 0.095 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

A4 0 0.1368 0 0 0 0 0 0.467 0 0 0 0 0 0.16 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

B1 0 0 0.4000 0 0 0 0 0 0.5000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0B2 0 0 0.4000 0 0 0 0 0 0.2500 0.7500 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

B3 0 0 0.2000 0 0 0 0 0 0.2500 0.2500 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

C1 0 0 0 0.2500 0 0 0 0 0 0.297 0 1 0 0.2500 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0C2 0 0 0 0.5000 0 0 0 0 1 0.54 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

C3 0 0 0 0.2500 0 0 0 0 0 0.163 0 0 0 0.7500 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

A11 0 0 0 0 0.3475 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0A12 0 0 0 0 0.3475 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0A13 0 0 0 0 0.1201 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0A14 0 0 0 0 0.0647 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

A15 0 0 0 0 0.1201 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

A21 0 0 0 0 0 0.2308 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0A22 0 0 0 0 0 0.4615 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0A23 0 0 0 0 0 0.2308 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

A24 0 0 0 0 0 0.0769 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

A31 0 0 0 0 0 0 0.3000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0A32 0 0 0 0 0 0 0.1000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0A33 0 0 0 0 0 0 0.3000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

A34 0 0 0 0 0 0 0.3000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

A41 0 0 0 0 0 0 0 0.5000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0A42 0 0 0 0 0 0 0 0.2500 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

A43 0 0 0 0 0 0 0 0.2500 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

B11 0 0 0 0 0 0 0 0 0.5000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

B12 0 0 0 0 0 0 0 0 0.5000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

B21 0 0 0 0 0 0 0 0 0 0.3333 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0B22 0 0 0 0 0 0 0 0 0 0.3333 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0B23 0 0 0 0 0 0 0 0 0 0.1667 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

B24 0 0 0 0 0 0 0 0 0 0.1667 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

B31 0 0 0 0 0 0 0 0 0 0 0.3333 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

B32 0 0 0 0 0 0 0 0 0 0 0.6667 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0

C11 0 0 0 0 0 0 0 0 0 0 0 0.6000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0C12 0 0 0 0 0 0 0 0 0 0 0 0.2000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0

C13 0 0 0 0 0 0 0 0 0 0 0 0.2000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0

C21 0 0 0 0 0 0 0 0 0 0 0 0 0.2000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0C22 0 0 0 0 0 0 0 0 0 0 0 0 0.4000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0

C23 0 0 0 0 0 0 0 0 0 0 0 0 0.4000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0

C31 0 0 0 0 0 0 0 0 0 0 0 0 0 0.3333 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0C32 0 0 0 0 0 0 0 0 0 0 0 0 0 0.3333 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0

C33 0 0 0 0 0 0 0 0 0 0 0 0 0 0.3333 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0

I1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.2697 0.2000 0.2156 0.2264 0.2889 0.1869 0.1750 0.1250 0.1237 0.1084 0.1084 0.1237 0.1667 0.1429 0.1429 0.1429 0.1199 0.0450 0.1237 0.0655 0.1667 0.2000 0.0780 0.3309 0.3325 0.1367 0.1367 0.1429 0.1237 0.1667 0.1237 0.2000 0.2500 1 0 0 0 0I2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0682 0.0780 0.0801 0.0558 0.0727 0.0534 0.0636 0.1250 0.1237 0.1084 0.1084 0.1237 0.1667 0.1429 0.1429 0.1429 0.1199 0.0670 0.1237 0.0655 0.1667 0.2000 0.0780 0.3309 0.0896 0.4030 0.4030 0.1429 0.1237 0.1667 0.1237 0.2000 0.2500 0 1 0 0 0I3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.1688 0.1255 0.1293 0.4904 0.1228 0.1213 0.1077 0.1250 0.1237 0.1084 0.1084 0.2343 0.1667 0.1429 0.1429 0.1429 0.2101 0.2191 0.1237 0.2500 0.1667 0.2000 0.1342 0.1985 0.3325 0.1367 0.1367 0.1429 0.1237 0.1667 0.1237 0.2000 0.2500 0 0 1 0 0I4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.4252 0.5459 0.5231 0.0867 0.4429 0.5871 0.5900 0.5000 0.2343 0.4737 0.2011 0.1237 0.3333 0.4286 0.4286 0.4286 0.0706 0.1480 0.2343 0.1094 0.1667 0.2000 0.4882 0.0844 0.2012 0.0791 0.0791 0.4286 0.2343 0.1667 0.3945 0.2000 0.1250 0 0 0 1 0

I5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0682 0.0507 0.0518 0.1407 0.0727 0.0513 0.0636 0.1250 0.3945 0.2011 0.4737 0.3945 0.1667 0.1429 0.1429 0.1429 0.4795 0.5210 0.3945 0.5096 0.3333 0.2000 0.2215 0.0553 0.0443 0.2444 0.2444 0.1429 0.3945 0.3333 0.2343 0.2000 0.1250 0 0 0 0 1



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EDITORIAL - Technical University of Valencia