Stochastic Multiplicative DEA for Estimating Most Productive Scale Size

In this paper, stochastic multiplicative data envelopment analysis (MDEA) model under variable return to scale (VRS) technology in the presence of log-normal distribution is proposed for estimating most productive scale size (MPSS). Banker and Maindiratta introduced MPSS pattern in MDEA model. The MDEA model requires that the values for all inputs and outputs be known exactly. But this assumption is not always correct, because data in many practical situations cannot be precisely measured. One of the most important methods, when we’re dealing with imprecise data is considering stochastic data. Moreover, for solving stochastic model, a deterministic equivalent is obtained and also stochastic α−MPSS is defined for decision making units (DMUs). Finally, an example of the systems reliability is presented to demonstrate our proposed modeling idea and its efficiency.