Optimizing Sustainable and Resilient Electric Vehicle Battery Recycling Network: Insights from Fourth-Party Logistics

Mingqiang Yin, Zhaolin Zhang, Liyan Wang (), Xiwang Guo, Xiaohu Qian and Muhammad Kamran
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Mingqiang Yin: School of Information and Control Engineering, Liaoning Petrochemical University, Fushun 113001, China
Zhaolin Zhang: School of Information and Control Engineering, Liaoning Petrochemical University, Fushun 113001, China
Liyan Wang: School of Information and Control Engineering, Liaoning Petrochemical University, Fushun 113001, China
Xiwang Guo: School of Information and Control Engineering, Liaoning Petrochemical University, Fushun 113001, China
Xiaohu Qian: Research Institute of Business Analytics and Supply Chain Management, College of Management, Shenzhen University, Shenzhen 518060, China
Muhammad Kamran: Research Institute of Business Analytics and Supply Chain Management, College of Management, Shenzhen University, Shenzhen 518060, China

Sustainability, 2025, vol. 17, issue 21, 1-27

Abstract: With the increasing scarcity of critical resources, competition in the electric vehicle battery (EVB) recycling market has intensified, and the strategic establishment of efficient and resilient recycling networks is increasingly vital for maintaining raw material security. Although existing studies have explored electric vehicle battery recycling network design (EVBRND), the impact of facility disruption risks on network decisions is rarely analyzed. This study explores a novel resilient EVBRND problem under disruption risk from the perspective of fourth-party logistics. To cope with disruptions, capacity backups, multi-source allocation, multiple third-party logistics (3PL), multiple transportation routes and facility fortification strategies are systematically integrated. A two-stage stochastic programming model is developed to characterize the problem, which is subsequently reformulated into a mixed-integer linear programming model using a scenario-based approach. To overcome the computational complexity resulting from the enlarged scenario set and the additional binary variables introduced by 3PL selection, a scenario reduction and decomposition-based heuristic (SRDBH) algorithm is developed, which integrates Lagrangian relaxation, conditional relaxation, scenario reduction, and the adaptive subgradient method. The proposed model and algorithm are validated through a real-world case study. Computational results confirm that the SRDBH algorithm achieves superior performance compared with CPLEX. Furthermore, sensitivity analyses highlight the critical role of flexible risk-mitigation configurations in balancing cost minimization with the enhancement of network resilience.

Keywords: resilience; recycling network; electric vehicle battery; heuristic algorithm; fourth-party logistics (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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