Some comments on improving discriminating power in data envelopment models based on deviation variables framework

Article


Mahdiloo, Mahdi, Lim, Sungmook, Duong, Thach-Thao and Harvie, Charles. 2021. "Some comments on improving discriminating power in data envelopment models based on deviation variables framework." European Journal of Operational Research. 295 (1), pp. 394-397. https://doi.org/10.1016/j.ejor.2021.02.056
Article Title

Some comments on improving discriminating power in data envelopment models based on deviation variables framework

ERA Journal ID148
Article CategoryArticle
AuthorsMahdiloo, Mahdi (Author), Lim, Sungmook (Author), Duong, Thach-Thao (Author) and Harvie, Charles (Author)
Journal TitleEuropean Journal of Operational Research
Journal Citation295 (1), pp. 394-397
Number of Pages4
Year2021
Place of PublicationNetherlands
ISSN0377-2217
1872-6860
Digital Object Identifier (DOI)https://doi.org/10.1016/j.ejor.2021.02.056
Web Address (URL)https://www.sciencedirect.com/science/article/pii/S0377221721001697
Abstract

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.

KeywordsCross-inefficiency; Data envelopment analysis; Deviation variables; Discriminating power; Ranking
ANZSRC Field of Research 2020469999. Other information and computing sciences not elsewhere classified
Byline AffiliationsUniversity of Wollongong
Dongguk University, Korea
Institution of OriginUniversity of Southern Queensland
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