Developing a Stochastic Input Oriented Data Envelopment Analysis (SIODEA) Model

Data Envelopment Analysis (DEA) is a powerful quantitative tool that provides a means to obtain useful information about efficiency and performance of firms, organizations, and all sorts of functionally similar, relatively autonomous operating units, known as Decision Making Units (DMU). Usually the investigated DMUs are characterized by a vector of multiple inputs and multiple outputs. Unfortunately, not all inputs and/or outputs are deterministic; some could be stochastic. The main concern in this paper is to develop an algorithm to help any organization for evaluating their performance given that some inputs are stochastic. The developed algorithm is for a Stochastic Input Oriented Model based on the Chance Constrained Programming, where the stochastic inputs are normally distributed, while the remaining inputs and all outputs are deterministic.