FUZZY DATA ENVELOPMENT ANALYSIS: REVIEW AND SOME NEW RESULTS

Data envelopment analysis (DEA) is a widely used mathematical programming technique that compares the inputs and outputs of a set of homogenous decision making units (DMUs) by evaluating their relative efficiency. In the conventional DEA model, all the data are known precisely or given as crisp values. However, the observed values of the input and output data in real-world problems are sometimes imprecise or vague. In addition, performance measurement in the conventional DEA method is based on the assumption that inputs should be minimized and outputs should be maximized. In this study, we first classify the solution methods for solving fuzzy data envelopment analysis into several groups and then explore the weakness and strength of each group in details. Finally, we give some new results leading to a new approach for measuring of efficiencies of DMUs with fuzzy inputs and outputs