As a technique based on mathematical programming, Data Envelopment Analysis (DEA) is used for evaluating the efficiency of homogeneous Decision Making Units (DMUs). DEA models need accurate input and output data. In many situations, on the one hand, accurate measurement of inputs and outputs is difficult due to their volatility and complexity. This conflict results in uncertain DEA models. Its main problem is transformation of deterministic equivalent of stochastic model into quadratic programming, time-consuming and complexity and it requires presuppositions. By means of Bi-objective multiple criteria DEA (Bio-MCDEA) model that considers stochastic data, our proposed model reduces some of these problems and facilitates problem solving through presenting primary presupposition and final linear model. The efficiency score of DMUs is determined by applying stochastic Bio- MCDEA model. Eventually, we used the data of seventeen Iranian electricity distribution companies to illustrate the methods developed in the present paper. Manuscript profile

The BCC model is studied in this paper in an uncertain environment where uncertain inputs and outputs were belief degree-based uncertainty‚ useful for the cases for which no historical information of an uncertain event is available. As the solution method‚ the uncertain BCC model was converted to a crisp form using two approaches, separately. Finally, an applied example regarding the Iranian Banking system is presented to document the proposed models. Manuscript profile