Determining most productive scale size with fuzzy stochastic input-output orientation model in DEA

The classic data envelopment analysis (DEA) requires that the values for all inputs and outputs be known exactly. However, in many real applications cannot be precisely measured. As real situations requires the consideration of both, fuzziness and randomness simultaneously. To remove this weakness in classic DEA model, we incorporate fuzzy and stochastic concepts. Therefore, this research studies most productive scale size with considering fuzzy stochastic constraints in DEA. In the present study we have developed a procedure for dealing with fuzzy stochastic model in estimating most productive scale size. To solve the fuzzy stochastic model, a deterministic equivalent is obtained. Although the deterministic equivalent is non-linear, it can be converted to a quadratic program. Furthermore, an empirical example is implemented to highlight the method. Performances are evaluated based on their scale sizes in classic and fuzzy stochastic DEA.

Keywords:

DEA , Probability of a fuzzy event , Fuzzy stochastic model , Most productive scale size (MPSS)