A Robust Credibility DEA Model with Fuzzy Perturbation Degree: An Application to Hospitals Performance

Abstract

Performance evaluation enables decision makers (DMs) to have a better view about the weaknesses and strengths of leading units to improve efficiencies as a crucial goal. Data envelopment analysis (DEA) is the most popular technique to measure performance efficiency of decision making units (DMUs). However, conventional DEA is unable to consider uncertainty of input and output data in the evaluations. In this study, in order to address uncertainty in data, a robust credibility DEA (RCDEA) model has been introduced. First, a fuzzy credibility approach is used to construct fuzzy data. Then, a robust optimization approach is applied to consider uncertainty in constructing fuzzy sets. Moreover, perturbation level is considered as exact and fuzzy values. To illustrate the capability of the proposed model, 28 hospitals are evaluated in northwestern region of Iran and results are analyzed. According to the results, as perturbation degree increases, DMUs get normalized lower efficiencies and vise-versa.

Publication DOI: https://doi.org/10.1016/j.eswa.2021.116021
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, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/. Funding: The article was prepared within the framework of the Basic Research Program at HSE University.
Uncontrolled Keywords: Data envelopment analysis,Robust optimization,Fuzzy sets,Efficiency
Publication ISSN: 1873-6793
Full Text Link: https://www.sci ... 957417421013683
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PURE Output Type: Article
Published Date: 2022-03-01
Published Online Date: 2021-10-06
Accepted Date: 2021-10-01
Authors: Omrani, Hashem
Alizadeh, Arash
Emrouznejad, Ali (ORCID Profile 0000-0001-8094-4244)
Teplova, Tamara

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Version: Accepted Version

Access Restriction: Restricted to Repository staff only until 6 October 2022.

License: Creative Commons Attribution Non-commercial No Derivatives


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