The survey of data envelopment analysis models in fuzzy stochastic environments

One of the best techniques for evaluating the performance of organizations is data envelopment analysis. Data Envelopment Analysis (DEA) is a nonparametric method for evaluating the performance of decisionmaking units (DMUs) that recognizes the relative performance of DMUs based on mathematical programming. The classic DEA model was initially formulated for optimal inputs and outputs, But in realworld problems, the values observed from input and output data are often ambiguous and random. In fact, decisionmakers may be faced with a specific hybrid environment where there are fuzziness and randomness in the problem. To overcome this problem, data envelopment analysis models in the random fuzzy environment have been proposed. Although the DEA has many advantages, one of the disadvantages of this method is that the classic DEA does not actually give us a definitive conclusion and does not allow random changes in input and output. In this research data envelopment analysis models in fuzzy random environments is reviewed.