Author Identifier (ORCID)
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.
Keywords
belief degree, data envelopment analysis, expected value model, optimal subgroup selection, petrochemical industry, uncertain data envelopment analysis
Document Type
Journal Article
Date of Publication
1-1-2026
ISSN
21460957
Volume
16
Issue
1
Publication Title
An International Journal of Optimization and Control: Theories & Applications
Publisher
AccScience Publishing
School
School of Business and Law
RAS ID
99268
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License
First Page
246
Last Page
264
Recommended Citation
Niroomand, S., Saleh, H., Shafiee, M., Pamucar, D., & Mahmoodirad, A. (2025). Optimizing subgroup selection in petrochemical industries: A robust data envelopment analysis approach for uncertainty management. An International Journal of Optimization and Control: Theories & Applications, 16(1), 246–264. https://doi.org/10.36922/IJOCTA025310138