A Stochastic Data Envelopment Analysis Model Considering Variation in Input and Output Variables

Data envelopment analysis (DEA) is a nonparametric method in is used to measure the relative efficiency of comparable institutions and also used for benchmarking in operations management. There is a weakness in conventional DEA models that it does not allow uncertainty variations in input and output variables however, in many real life applications variables are usually vague. As a result, DEA efficiency measurement may be sensitive to such variations. Therefore, in this paper, input oriented model is one of the classic models in DEA going to develop in stochastic DEA that allow some of input and output variables have random in nature. Finally, an illustrative example has been presented