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

Hashem Omrani, Arash Alizadeh, Ali Emrouznejad*, Tamara Teplova

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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.
Original languageEnglish
Article number116021
JournalExpert Systems with Applications
Volume189
Early online date6 Oct 2021
DOIs
Publication statusE-pub ahead of print - 6 Oct 2021

Bibliographical note

© 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.

Keywords

  • Data envelopment analysis
  • Robust optimization
  • Fuzzy sets
  • Efficiency

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