Solving generalized fuzzy data envelopment analysis model: a parametric approach

Ali Asghar Foroughi () and Roohollah Abbasi Shureshjani ()
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Ali Asghar Foroughi: University of Qom
Roohollah Abbasi Shureshjani: University of Qom

Central European Journal of Operations Research, 2017, vol. 25, issue 4, No 9, 889-905

Abstract: Abstract 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.

Keywords: Data envelopment analysis; Fuzzy numbers; GFDEA model; Parametric programming (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (4)

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DOI: 10.1007/s10100-016-0448-5

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