A new approach based on alpha cuts for solving data envelopment analysis model with fuzzy stochastic inputs and outputs

Data Envelopment Analysis (DEA) is a widely used technique for measuring the relative efficiencies of homogenous Decision Making Units (DMUs) with multiple inputs and multiple outputs. These factors may be evaluated in fuzzy or stochastic environment. Hence, the classic structures of DEA model may be changed where in two fold fuzzy stochastic environment. For instances, linearity, feasibility and normal domain of efficiency scores (input orientation) between zero and one are some of these properties. In this paper, a new approach based on alpha cuts is proposed for evaluating decision making units with fuzzy stochastics inputs and outputs. The proposed approach modifies these weaknesses to solve DEA models with fuzzy stochastic parameters. A numerical example is given to illustrate the features and the applicability of the proposed model.