Fuzzy data envelopment analysis (DEA): a possibility approach

Abstract Evaluating the performance of activities or organizations by traditional data envelopment analysis (DEA) models requires crisp input/output data. However, in real-world problems inputs and outputs are often imprecise. This paper develops DEA models using imprecise data represented by fuzzy sets (i.e., “fuzzy DEA” models). It is shown that fuzzy DEA models take the form of fuzzy linear programming which typically are solved with the aid of some methods to rank fuzzy sets. As an alternative, a possibility approach is introduced in which constraints are treated as fuzzy events. The approach transforms fuzzy DEA models into possibility DEA models by using possibility measures of fuzzy events (fuzzy constraints). We show that for the special case, in which fuzzy membership functions of fuzzy data are of trapezoidal types, possibility DEA models become linear programming models. A numerical experiment is used to illustrate the approach and compare the results with those obtained with alternative approaches.