Data Envelopment Analysis Model with Decision Makers’ Preferences: A Robust Credibility Approach


Data envelopment analysis (DEA) is one of the widely used methods to measure the efficiency scores of decision making units (DMUs). Conventional DEA is unable to consider both uncertainty in data and decision makers’ (DMs) judgments in the evaluations. This study, to address the shortcomings of the conventional DEA, proposes a new best worst method (BWM)- robust credibility DEA (BWM-RCDEA) model to estimate the efficiency scores of DMUs considering DMs’ preferences and uncertain data, simultaneously. First, to handle uncertainty in input and output variables, fuzzy credibility model has been applied. Additionally, uncertainty in constructing fuzzy sets is modeled using robust optimization with fuzzy perturbation degree. In this paper, two new types of RCDEA models are proposed: RCDEA model with exact perturbation in fuzzy inputs and outputs and RCDEA model with fuzzy perturbation in fuzzy inputs and outputs. In addition, to deal with flexibility of weights and incorporating DMs’ judgement into the RCDEA model, a bi-objective BWM-RCDEA model is introduced. Finally, the proposed bi-objective model is solved using min–max approach. To illustrate the usefulness and capability of the proposed model, efficiency scores of 39 distribution companies in Iran is investigated and results are analyzed and discussed. Finally, based on the results, recommendations have been made for policy makers.

Publication DOI:
Divisions: College of Business and Social Sciences > Aston Business School > Operations & Information Management
College of Business and Social Sciences > Aston Business School
Additional Information: © Springer Nature B.V. 2021. The final publication is available at Springer via
Publication ISSN: 1572-9338
Last Modified: 08 Jul 2024 08:08
Date Deposited: 18 Oct 2021 09:59
Full Text Link:
Related URLs: https://link.sp ... 479-021-04262-2 (Publisher URL)
PURE Output Type: Article
Published Date: 2021-10-25
Published Online Date: 2021-10-25
Accepted Date: 2021-08-30
Authors: Omrani, Hashem
Alizadeh, Arash
Emrouznejad, Ali (ORCID Profile 0000-0001-8094-4244)
Teplova, Tamara



Version: Accepted Version

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