AccScience Publishing / IJOCTA / Online First / DOI: 10.36922/IJOCTA025310138
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RESEARCH ARTICLE

Optimizing subgroup selection in petrochemical industries: A robust data envelopment analysis approach for uncertainty management

Sadegh Niroomand1* ,  Hilda Saleh2 ,  Morteza Shafiee3 ,  Dragan Pamucar4* ,  Ali Mahmoodirad5
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1 Department of Industrial Engineering, Firouzabad Higher Education Center, Shiraz University of Technology, Shiraz, Iran
2 Department of Mathematics, CT. C., Islamic Azad University, Tehran, Iran
3 School of Business and Law, Edith Cowan University, Perth, Australia
4 Sustainability Competence Centre, Széchenyi István University, Győr, Hungary
5 Department of Mathematics, Bab. C., Islamic Azad University, Babol, Iran
IJOCTA, 025310138 https://doi.org/10.36922/IJOCTA025310138
Received: 3 August 2025 | Revised: 16 October 2025 | Accepted: 20 October 2025 | Published online: 18 December 2025
© 2025 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( https://creativecommons.org/licenses/by-nc/4.0/ )
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Abstract

This study addresses the critical challenge faced by organizations in selecting an optimal subgroup of decision-making units (DMUs). Such a selection procedure can significantly influence efficiency, profitability, and strategic development. Recognizing the limitations of existing methods in handling inexact data and incorporating managerial preferences, this study proposes a novel framework that integrates data envelopment analysis (DEA) with binary linear programming models. The model applies belief-degree–based representations of uncertainty to capture imprecise inputs and outputs. For this model, two solution approaches—namely, chance-constrained programming and expected value approaches—were developed. These approaches are suitable for real world applications using standard optimization software. The effectiveness of the proposed method was validated through a case study in Iran’s petrochemical industry, where it successfully identified the optimal technology for a new refinery unit while balancing efficiency and profitability under uncertainty. This work is the first study in the literature to combine DEA and binary linear programming under belief-degree–based uncertainty for DMU selection, offering a systematic, practical, and computationally efficient solution, with recommendations for future research to explore alternative uncertainty modeling techniques and broader industrial applications.

Graphical abstract
Keywords
Belief degree
Data envelopment analysis
Expected value model
Optimal subgroup selection
Petrochemical industry
Uncertain data envelopment analysis
Funding
None.
Conflict of interest
The authors certify that none of our interests is in conflict with one another. Each author attests to having contributed enough to the work to accept public accountability for its content, including assistance with the concept, design, analysis, writing, and editing of the text. All individuals who meet the standards for authorship are listed as authors. Additionally, each author attests that, prior to its publishing in the journal, none of the content in this or related works has been submitted to or published in any other magazine.
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An International Journal of Optimization and Control: Theories & Applications, Electronic ISSN: 2146-5703 Print ISSN: 2146-0957, Published by AccScience Publishing
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