In this study, both optimistic and pessimistic approaches of data envelopment analysis are applied to propose an equitable ranking method in fuzzy environments. To this end, we suppose that the sum of efficiency scores of all decision making units (DMUs) equals to unity. Using the worst-best and best-worst approaches, the minimum and maximum possible efficiency scores of each DMU are estimated at some $lpha$-levels. Then, a number of such scores are used to construct the corresponding fuzzy score. Finally, using a defuzzification method the obtained fuzzy score is transformed into crisp score. DMUs are ranked according to their crisp scores.
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