Data envelopment analysis (DEA) is a powerful technique for performance evaluation of decision making units (DMUs). One of the main objectives that is followed in performance evaluation is discriminating among efficient DMUs to provide a complete ranking of DMUs. DEA su More
Data envelopment analysis (DEA) is a powerful technique for performance evaluation of decision making units (DMUs). One of the main objectives that is followed in performance evaluation is discriminating among efficient DMUs to provide a complete ranking of DMUs. DEA successfully divides them into two categories: efficient DMUs and inefficient DMUs. The DMUs in the efficient category have identical efficiency score. But the question that raises here is in evaluation. Where several DMUs have the equal efficiency, which unit performs better and how can we rank these efficient units, Different methods have been presented for ranking the efficient units. In this paper, we propose a method for calculating an efficiency of DMUs by comparing with the bad benchmark line. Our approach obtain common set of weights to create the best efficiency score, such that the amount of DMUs that are efficient is less than that of other models. If we have more than one efficient DMU, we can rank them by the same model and it isn't necessary to use another ranking method.
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This paper introduces two virtual Decision Making Units (DMUs) called ideal point and anti-ideal point, Then calculates distances of each DMU to the ideal and anti-ideal point. The two distinctive distances are combined to form a comprehensive index called the relative More
This paper introduces two virtual Decision Making Units (DMUs) called ideal point and anti-ideal point, Then calculates distances of each DMU to the ideal and anti-ideal point. The two distinctive distances are combined to form a comprehensive index called the relative closeness (RC) just like the TOPSIS approach. The RC index is used as an overall ranking for all the DMUs. Then, this method compares with AP [1], Wang et al. [8], and Wu [9] methods. The proposed method is more simple and better than other methods and also it doesn’t have drawbacks of the previous ranking methods.
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