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Some comments on improving discriminating power in data envelopment models based on deviation variables framework
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AI
DOI：10.1016/j.ejor.2021.02.056.png)
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Abstract
En 中文
Ghasemi, Ignatius, and Rezaee (2019) (Improving discriminating power in data envelopment models based on deviation variables framework. European Journal of Operational Research 278, 442- 447) propose a procedure for ranking efficient units in data envelopment analysis (DEA) based on the deviation variables framework. They claim that their procedure improves the discriminating power of DEA and can be an alternative to the super-efficiency model that is well-known to have the infeasibility problem and the cross-efficiency approach which suffers from the presence of multiple optimal solutions. However, we demonstrate, in this short note, that their procedure is developed based upon inappropriate use of deviation variables which leads to the development of a ranking approach that does not meet their expectations and as a result, an unreasonable ranking of decision making units (DMUs). We also show that the use of deviation variables, if interpreted and used correctly, can lead to developing a cross-inefficiency matrix and approach. (c) 2021 Elsevier B.V. All rights reserved.
Keywords:
Data envelopment analysis
Ranking
Discriminating power
Deviation variables
Cross-inefficiency
Journal
IF:
6
Papers:
2.2W
Citations:
6.4W