A novel best worst method robust data envelopment analysis:Incorporating decision makers’ preferences in an uncertain environment

Abstract

Data Envelopment Analysis (DEA) has been widely applied in measuring the efficiency of Decision-Making Units (DMUs). The conventional DEA has three major drawbacks: a) it does not consider Decision Makers’ (DMs) preferences in the evaluation process, b) DMUs in this model are flexible in weighting the criteria to reach the maximum possible efficiency, and c) it ignores the uncertainty in data. However, in many real-world applications, data are uncertain as well as imprecise and managers want to impose their opinions in decision-making procedure. To address these problems, this paper develops a novel multi-objective Best Worst Method (BWM)-Robust DEA (RDEA) for incorporating DMs’ preferences into DEA model in an uncertain environment. The proposed model tries to provide a new efficiency score which is more reliable and compatible with real problems by taking the advantages of the BWM to apply experts’ opinions and RDEA to model the uncertainty This bi-objective BWM-RDEA model is solved utilizing amin-max technique and so as to illustrate its usefulness, this model is implemented for assessing Iranian airlines.

Publication DOI: https://doi.org/10.1016/j.orp.2021.100184
Divisions: College of Business and Social Sciences > Aston Business School > Operations & Information Management
College of Business and Social Sciences > Aston Business School
Additional Information: © 2021 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license.
Uncontrolled Keywords: Airline Efficiency,Best Worst Method (BWM),Data Envelopment Analysis (DEA),Robust Optimization,Statistics and Probability,Strategy and Management,Management Science and Operations Research,Control and Optimization
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Related URLs: https://www.sci ... 0075?via%3Dihub (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2021
Published Online Date: 2021-04-15
Accepted Date: 2021-03-30
Authors: Omrani, Hashem
Valipour, Mahsa
Emrouznejad, Ali (ORCID Profile 0000-0001-8094-4244)

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