New approaches for solving fuzzy data envelopment analysis
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One of the most appropriate approaches for determining the efficiency of decision-making units is Data Envelopment Analysis (DEA). When data is described uncertainty and vaguely, the necessity of using fuzzy theory appears. In this study, new approaches are proposed to solve the fuzzy DEA model. The fuzzy DEA model is first transformed into an interval DEA by the nearest interval approximation of a fuzzy number. The proposed approach is based on the marked distance for comparison and ranking of efficiency. Also, we study fuzzy DEA models, which use possibility and necessity to measure fuzzy events. The necessity measure estimates the amount of necessity for each fuzzy set. In this research, because the possibility measure and necessity measure are each other dual, comparing the results of their efficiency, it is more reliable to determine efficient units. We give numerical examples to examine the proposed approaches and compare the models.
Abstract
One of the most appropriate approaches for determining the efficiency of decision-making units is Data Envelopment Analysis (DEA). When data is described uncertainty and vaguely, the necessity of using fuzzy theory appears. In this study, new approaches are proposed to solve the fuzzy DEA model. The fuzzy DEA model is first transformed into an interval DEA by the nearest interval approximation of a fuzzy number. The proposed approach is based on the marked distance for comparison and ranking of efficiency. Also, we study fuzzy DEA models, which use possibility and necessity to measure fuzzy events. The necessity measure estimates the amount of necessity for each fuzzy set. In this research, because the possibility measure and necessity measure are each other dual, comparing the results of their efficiency, it is more reliable to determine efficient units. We give numerical examples to examine the proposed approaches and compare the models.
참고문헌 (28)
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