Multi-Criteria Decision Making for Ranking Decision Making Units

Abstract

There is no doubt the Data Envelopment Analysis (DEA) is a powerful method for the efficiency evaluation of Decision Making Units (DMUs) with multiple inputs and outputs. Despite its usefulness, DEA has some notable limitations. A significant drawback with this approach is that inability to fully rank the DMUs. In the extant literature, different methods for this purpose have been suggested. While, in the traditional method the first step for the DEA approach is used, and results of this step are input for the DEA ranking method in the second step. To reduce the computational complexity of the traditional method, a new Multiple Criteria Decision Making (MCDM) approach is proposed in this article. In the proposed approaches, one step can achieve full ranking for all DMUs. The results show that although out of 20 DMUs are first in the final ranking ordered by the DEA, the author proposed methods can consider full ranking. Agreement of the proposed methods with the existing approaches are measured by the Spearman's rank correlation coefficient technique. The findings of this study reveal that TOPSIS, Neo-TOPSIS, and AHP ranking results are consistent with the DEA ranking method. Therefore, these proposed methods appear as the possible alternatives to the DEA and DEA ranking models.