Solving generalized fuzzy data envelopment analysis model: a parametric approach
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Data envelopment analysis (DEA) is a non-parametric technique to assess the performance of a set of homogeneous decision making units (DMUs) with common crisp inputs and outputs. Regarding the problems that are modelled out of the real world, the data cannot constantly be precise and sometimes they are vague or fluctuating. So in the modelling of such data, one of the best approaches is using the fuzzy numbers. Substituting the fuzzy numbers for the crisp numbers in DEA, the traditional DEA problem transforms into a fuzzy data envelopment analysis (FDEA) problem. Different methods have been suggested to compute the efficiency of DMUs in FDEA models so far but the most of them have limitations such as complexity in calculation, non-contribution of decision maker in decision making process, utilizable for a specific model of FDEA and using specific group of fuzzy numbers. In the present paper, to overcome the mentioned limitations, a new approach is proposed. In this approach, the generalized FDEA problem is transformed into a parametric programming, in which, parameter selection depends on the decision maker’s ideas. Two numerical examples are used to illustrate the approach and to compare it with some other approaches.
KeywordsData envelopment analysis Fuzzy numbers GFDEA model Parametric programming
The authors are grateful for the comments and suggestions made by two anonymous reviewers, which helped to improve this paper. By the agreement between EURO and IFORS, second author could be sponsored by IFORS from a non-EURO member society to participate in ESI XXXII and this paper was first presented there. Also, his travel report is published in IFORS newsletter, September 2015. Thanks to IFORS and ESI organizing committee.
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