Advancing the foundations of data envelopment analysis through fuzzy multi-objective optimization

Weiming Wang; Mohammadreza Feylizadeh; Morteza Bagherpour; Weiming Wang; Mohammadreza Feylizadeh; Morteza Bagherpour

doi:10.3934/jimo.2026164

Journal of Industrial and Management Optimization

2026, Volume 22, Issue 10: 4735-4769. doi: 10.3934/jimo.2026164

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Advancing the foundations of data envelopment analysis through fuzzy multi-objective optimization

1.
School of Business Administration, Jiangxi University of Finance and Economics, Nanchang, Jiangxi, 330013, China
2.
Department of Industrial Engineering, Shi.C., Islamic Azad University, Shiraz, Iran
3.
Department of Engineering and Technology Management, University of Pretoria, Pretoria, South Africa

Received: 31 December 2025 Revised: 25 June 2026 Accepted: 10 July 2026 Published: 07 September 2026
90B50, 90C08, 90C29, 90C70

Evaluating the performance of decision-making units (DMUs) in environments with multiple inputs and outputs remains a critical challenge, particularly when efficiency assessments must account for trade-offs and decision-maker preferences. Traditional Data Envelopment Analysis (DEA) models often reduce multidimensional performance into a single scalar efficiency score, limiting their ability to reflect complex decision contexts. To address this limitation, we proposed a novel framework that integrated DEA with Fuzzy Multi-Objective Linear Programming (FMOLP) and the Ordinal Priority Approach (OPA). The proposed model reformulated DEA within a multi-objective optimization framework, enabling simultaneous consideration of multiple performance criteria and incorporating preference information through ordinal rankings. The model's applicability was demonstrated using a dataset of renewable energy units, evaluated against economic and environmental indicators. The results showed that, compared to classical DEA and common-weight models, the proposed FMOLP–OPA framework provides a more comprehensive and discriminative assessment by capturing trade-offs among objectives and reflecting strategic priorities. The findings highlight that efficiency evaluation should move beyond purely technical measures toward preference-driven, multi-objective frameworks. The proposed approach offers enhanced decision support for complex real-world problems with multiple stakeholders and conflicting objectives.
- data envelopment analysis (DEA),
- fuzzy multi-objective optimization,
- ordinal priority approach (OPA)
Citation: Weiming Wang, Mohammadreza Feylizadeh, Morteza Bagherpour. Advancing the foundations of data envelopment analysis through fuzzy multi-objective optimization[J]. Journal of Industrial and Management Optimization, 2026, 22(10): 4735-4769. doi: 10.3934/jimo.2026164

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Abstract

Evaluating the performance of decision-making units (DMUs) in environments with multiple inputs and outputs remains a critical challenge, particularly when efficiency assessments must account for trade-offs and decision-maker preferences. Traditional Data Envelopment Analysis (DEA) models often reduce multidimensional performance into a single scalar efficiency score, limiting their ability to reflect complex decision contexts. To address this limitation, we proposed a novel framework that integrated DEA with Fuzzy Multi-Objective Linear Programming (FMOLP) and the Ordinal Priority Approach (OPA). The proposed model reformulated DEA within a multi-objective optimization framework, enabling simultaneous consideration of multiple performance criteria and incorporating preference information through ordinal rankings. The model's applicability was demonstrated using a dataset of renewable energy units, evaluated against economic and environmental indicators. The results showed that, compared to classical DEA and common-weight models, the proposed FMOLP–OPA framework provides a more comprehensive and discriminative assessment by capturing trade-offs among objectives and reflecting strategic priorities. The findings highlight that efficiency evaluation should move beyond purely technical measures toward preference-driven, multi-objective frameworks. The proposed approach offers enhanced decision support for complex real-world problems with multiple stakeholders and conflicting objectives.

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