Fuzzy data envelopment analysis: A new parametric approach

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 and utilizable for a specific group of fuzzy numbers. In the present paper to overcome the mentioned limitations, a new approach is proposed. In this approach, the FDEA problem transforms into a parametric programming in which, parameter selection depends on the decision maker‘s (DM) ideas. Two numerical examples are used to illustrate the approach and to compare it with some other approaches.

کلیدواژه ها:

Data envelopment analysis ، Fuzzy numbers ، FCCR model ، FBCC model ، Parametric programming