Solving generalized fuzzy data envelopment analysis model: a parametric approach

摘要: Data envelopment analysis (DEA) is a non-parametric technique to assess the performance of set homogeneous decision making units (DMUs) with common crisp inputs and outputs. Regarding problems that are modelled out real world, data cannot constantly be precise sometimes they vague or fluctuating. So in modelling such data, one best approaches using fuzzy numbers. Substituting numbers for DEA, traditional DEA problem transforms into (FDEA) problem. Different methods have been suggested compute efficiency DMUs FDEA models so far but most them limitations as complexity calculation, non-contribution maker process, utilizable specific model group In present paper, overcome mentioned limitations, new approach proposed. this approach, generalized transformed parametric programming, which, parameter selection depends on maker’s ideas. Two numerical examples used illustrate compare it some other approaches.