Logo image
Some comments on improving discriminating power in data envelopment models based on deviation variables framework
Journal article   Peer reviewed

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

Mahdi Mahdiloo, Sungmook Lim, Thao Duong and Charles Harvie
European journal of operational research, Vol.295(1), pp.394-397
2021

Abstract

Cross-inefficiency Data envelopment analysis Deviation variables Discriminating power Ranking Optimisation
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.

Details

UN Sustainable Development Goals (SDGs)

This output has contributed to the advancement of the following goals:

#9 Industry, Innovation and Infrastructure

Source: InCites

Metrics

InCites Highlights

These are selected metrics from InCites Benchmarking & Analytics tool, related to this output

Collaboration types
Domestic collaboration
International collaboration
Citation topics
6 Social Sciences
6.10 Economics
6.10.502 Data Envelopment Analysis
Web Of Science research areas
Management
Operations Research & Management Science
ESI research areas
Engineering
Logo image