Evaluating the efficiency and classifying the fuzzy data: A DEA based ‎approach

Data envelopment analysis (DEA) has been proven as an efficient technique to evaluate the performance of homogeneous decision making units (DMUs) where multiple inputs and outputs exist. In the conventional applications of DEA, the data are considered as specific numerical values with explicit designation of being an input or output. However, the observed values of the data are sometimes imprecise (i.e. input and output variables cannot be measured precisely) and data are sometimes flexible (measures with unknown status of being input or output are referred to as flexible measures in the literature). In the current paper a number of methods are proposed to evaluate the relative efficiency and to identify the status of fuzzy flexible measures. Indeed, the modified fuzzy DEA models are suggested to accommodate flexible measures. In order to obtain correct results, alternative optimal solutions are considered to deal with the fuzzy flexible measures. Numerical examples are used to illustrate the ‎procedure.‎

Keywords:

Data Envelopment Analysis , Fuzzy numbers , flexible measures , Inputs , ‎Outputs.‎