An Intelligent Fuzzy Multi-Criteria Framework for Joint Procurement Allocation and Logistics Network Design under Sustainability and Risk Considerations

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Vol. 2 No. 2 (2026)

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Ershadi, M. M., & Rahimi Rise, Z. (2026). An Intelligent Fuzzy Multi-Criteria Framework for Joint Procurement Allocation and Logistics Network Design under Sustainability and Risk Considerations. Transnational Supply Chain Research, 2(2), 50-93. https://doi.org/10.65773/tscr.2.2.145

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Authors

Mohammad Mahdi Ershadi*
Department of Industrial Engineering and Management Systems, Amirkabir University of Technology, Tehran, Iran
Zeinab Rahimi Rise
Department of Industrial Engineering and Management Systems, Amirkabir University of Technology, Tehran, Iran

Abstract

This study proposes an integrated fuzzy multi-objective decision-support framework for the simultaneous optimization of procurement allocation and logistics network design in multi-echelon supply chains. The framework explicitly captures the interdependence between sourcing and transportation decisions while balancing four strategic objectives: minimization of total operational cost, minimization of supplier-related quality defects, maximization of supplier sustainability performance, and minimization of supply chain disruption risk. To address uncertainty in production capacities and operational conditions, the model incorporates fuzzy parameters and is solved using an Augmented Max–Min Fuzzy Multi-Objective Linear Programming (AMM-FMOLP) approach that generates balanced compromise solutions across conflicting objectives. The applicability of the proposed framework is demonstrated through a real-world case study involving a computer numerical control (CNC) machine-tool assembly supply chain consisting of 15 suppliers, 2 assembly facilities, 3 distribution centers, and 6 customer zones. The obtained compromise solution achieved utility values of 0.6667, 0.6820, 0.6747, and 0.6667 for cost, defect rate, sustainability performance, and disruption risk, respectively, resulting in a total operational cost of 26.586 million monetary units, a defect-rate index of 209.57, a sustainability score of 1,546.148, and a disruption-risk index of 214.223. Comparative scenario analyses revealed that single- and partial-objective optimization approaches yielded marginal improvements in individual objectives but produced less balanced overall performance. Sensitivity analyses further confirmed robustness of proposed framework under different conditions. Results demonstrate that proposed framework provides an effective mechanism for enhancing supply chain efficiency, sustainability, and resilience, making it a valuable decision-support tool for intelligent supply chain management in uncertain operating environments.

Keywords:
sustainable supply chain management, procurement allocation, logistics network design, fuzzy multi-objective optimization, effectivity and resilience

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