Revenue Efficiency Measurement with Undesirable Data in Fuzzy DEA

Most DEA models were initially considered only for crisp desirable inputs and outputs, but in the real world data have imprecise value, fuzzy concept is too important as imprecise and undesirable data. Moreover, the decision making units may have been some inputs and outputs index such undesirable data. Therefore, in this study, we introduce a method to evaluate revenue efficiency of DMUs when the data are desirable and undesirable in fuzzy DEA. For this purpose, we apply a-cut method and extension principle and obtain lower and upper bounds per each a ? [0,1] provides for assessment of revenue efficiency, Thus, the amount of fuzzy revenue efficiency is greater than one. Finally, a numerical example is presented for application of proposed method.