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

Mahdiloo, Mahdi and Lim, Sungmook and Duong, Thach-Thao ORCID: https://orcid.org/0000-0003-2294-3619 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. ISSN 0377-2217


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.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Faculty/School / Institute/Centre: No Faculty
Faculty/School / Institute/Centre: No Faculty
Date Deposited: 30 Mar 2022 03:47
Last Modified: 12 Apr 2022 23:27
Uncontrolled Keywords: Cross-inefficiency; Data envelopment analysis; Deviation variables; Discriminating power; Ranking
Fields of Research (2020): 46 INFORMATION AND COMPUTING SCIENCES > 4699 Other information and computing sciences > 469999 Other information and computing sciences not elsewhere classified
Identification Number or DOI: https://doi.org/10.1016/j.ejor.2021.02.056
URI: http://eprints.usq.edu.au/id/eprint/46974

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