A fuzzy DEA/AR approach to the selection of flexible manufacturing systems

Flexible Manufacturing System (FMS) offers opportunities for manufacturers to improve their technology, competitiveness, and profitability through a highly efficient and focused approach to manufacturing effectiveness. Data envelopment analysis (DEA) has been utilized as a multiple criteria tool for evaluation of FMSs. The concept of the assurance region (AR) is restricting the ratio of any two weights to some range to avoid the evaluated alternatives from ignoring or relying too much on any criterion in evaluation. In this paper, we develop a fuzzy DEA/AR method that is able to evaluate the performance of FMS alternatives when the input and output data are represented as crisp and fuzzy data. Based on Zadeh's extension principle, a pair of two-level mathematical programs is formulated to calculate the lower and upper bounds of the fuzzy efficiency score of the alternatives. We transform this pair of two-level mathematical programs into a pair of conventional one-level DEA/AR method to evaluate the FMS performance. An example illustrates the application of the proposed methodology.

[1]  A. Charnes,et al.  The non-archimedean CCR ratio for efficiency analysis: A rejoinder to Boyd and Färe☆ , 1984 .

[2]  Jian-Bo Yang,et al.  On the centroids of fuzzy numbers , 2006, Fuzzy Sets Syst..

[3]  E. Ertugrul Karsak A TWO-PHASE ROBOT SELECTION PROCEDURE , 1998 .

[4]  Markku Kuula,et al.  Selecting a flexible manufacturing system using multiple criteria analysis , 1991 .

[5]  Marc Roubens,et al.  Ranking and defuzzification methods based on area compensation , 1996, Fuzzy Sets Syst..

[6]  Chiang Kao,et al.  Fuzzy efficiency measures in data envelopment analysis , 2000, Fuzzy Sets Syst..

[7]  E. Ertugrul Karsak,et al.  A fuzzy multiple objective programming approach for the selection of a flexible manufacturing system , 2002 .

[8]  Toshiyuki Sueyoshi,et al.  A unified framework for the selection of a Flexible Manufacturing System , 1995 .

[9]  Russell G. Thompson,et al.  The role of multiplier bounds in efficiency analysis with application to Kansas farming , 1990 .

[10]  Cerry M. Klein,et al.  A simple approach to ranking a group of aggregated fuzzy utilities , 1997, IEEE Trans. Syst. Man Cybern. Part B.

[11]  Ching-Lai Hwang,et al.  Fuzzy Multiple Attribute Decision Making - Methods and Applications , 1992, Lecture Notes in Economics and Mathematical Systems.

[12]  Chiang Kao,et al.  Solving fuzzy transportation problems based on extension principle , 2004, Eur. J. Oper. Res..

[13]  Lucien Duckstein,et al.  Comparison of fuzzy numbers using a fuzzy distance measure , 2002, Fuzzy Sets Syst..

[14]  Toshiyuki Sueyoshi,et al.  Measuring the industrial performance of Chinese cities by data envelopment analysis , 1992 .

[15]  Shiang-Tai Liu,et al.  Optimization of a machining economics model with fuzzy exponents and coefficients , 2006 .

[16]  Cengiz Kahraman,et al.  Fuzzy multi-criteria evaluation of industrial robotic systems , 2007, Comput. Ind. Eng..

[17]  Hung T. Nguyen,et al.  A note on the extension principle for fuzzy sets , 1978 .

[18]  Jens Leth Hougaard,et al.  A simple approximation of productivity scores of fuzzy production plans , 2005, Fuzzy Sets Syst..

[19]  Teresa León,et al.  A fuzzy mathematical programming approach to the assessment of efficiency with DEA models , 2003, Fuzzy Sets Syst..

[20]  Shu-Cherng Fang,et al.  Fuzzy data envelopment analysis (DEA): a possibility approach , 2003, Fuzzy Sets Syst..

[21]  H. Zimmermann,et al.  Fuzzy Set Theory and Its Applications , 1993 .

[22]  Abraham Charnes,et al.  Measuring the efficiency of decision making units , 1978 .

[23]  Felix T.S. Chan,et al.  A fuzzy approach to operation selection , 1997 .

[24]  E. E. Karsak *,et al.  Practical common weight multi-criteria decision-making approach with an improved discriminating power for technology selection , 2005 .

[25]  E. Karsak,et al.  Fuzzy multi-criteria decision-making procedure for evaluating advanced manufacturing system investments , 2001 .

[26]  Nallan C. Suresh,et al.  Towards an integrated evaluation of flexible automation investments , 1990 .

[27]  Barton A. Smith,et al.  Comparative Site Evaluations for Locating a High-Energy Physics Lab in Texas , 1986 .

[28]  Moutaz Khouja,et al.  The use of data envelopment analysis for technology selection , 1995 .

[29]  David de la Fuente,et al.  A comparison of machine-learning algorithms for dynamic scheduling of flexible manufacturing systems , 2006, Eng. Appl. Artif. Intell..

[30]  L. Zadeh Fuzzy sets as a basis for a theory of possibility , 1999 .

[31]  Gisella Facchinetti,et al.  A characterization of a general class of ranking functions on triangular fuzzy numbers , 2004, Fuzzy Sets Syst..

[32]  Craig A. Nelson,et al.  A scoring model for flexible manufacturing systems project selection , 1986 .