With the rapid development of electric vehicles, a large number of end-of-life power batteries are entering the recycling stage. Under carbon emission constraints and uncertain disruption risks, the construction of an efficient, low-carbon, and resilient recycling network has become a critical issue. From a fourth-party logistics perspective, this study investigated an electric vehicle battery (EVB) recycling network design problem that integrates internal recycling operations with external resource collaboration, and a combinatorial auction framework with an embedded winner determination mechanism was introduced to enable dynamic allocation of external recycling capacity. On this basis, a two-stage stochastic programming model incorporating carbon emission constraints and disruption scenarios was developed to optimize network configuration and resource allocation decisions. To address the computational challenges of large-scale problems, a hybrid algorithm that integrates scenario reduction and Lagrangian relaxation based on the progressive hedging algorithm was proposed to improve solution efficiency and obtain high-quality solutions. Numerical experiments and validation were conducted based on a real-world case. The results indicate that the proposed SR-LR algorithm consistently achieves optimality gaps below 0.01% and obtains solutions within 3–37 seconds across all test instances. By comparison, CPLEX requires from 12.9 minutes to 2.35 hours for solvable instances and is unable to solve several large-scale scenarios within acceptable computational limits. These findings provide useful insights for planning and decision-making for EVB recycling networks in a low-carbon context.
Citation: Yingfu Zhang, Xuefeng Wang, Mingqiang Yin, Guodong Liang, Xiaohu Qian, Chunxi Han. Green electric vehicle battery recycling network design under supply chain disruptions: a 4PL-based approach with combinatorial auction-based winner determination[J]. Journal of Industrial and Management Optimization, 2026, 22(8): 3562-3604. doi: 10.3934/jimo.2026130
With the rapid development of electric vehicles, a large number of end-of-life power batteries are entering the recycling stage. Under carbon emission constraints and uncertain disruption risks, the construction of an efficient, low-carbon, and resilient recycling network has become a critical issue. From a fourth-party logistics perspective, this study investigated an electric vehicle battery (EVB) recycling network design problem that integrates internal recycling operations with external resource collaboration, and a combinatorial auction framework with an embedded winner determination mechanism was introduced to enable dynamic allocation of external recycling capacity. On this basis, a two-stage stochastic programming model incorporating carbon emission constraints and disruption scenarios was developed to optimize network configuration and resource allocation decisions. To address the computational challenges of large-scale problems, a hybrid algorithm that integrates scenario reduction and Lagrangian relaxation based on the progressive hedging algorithm was proposed to improve solution efficiency and obtain high-quality solutions. Numerical experiments and validation were conducted based on a real-world case. The results indicate that the proposed SR-LR algorithm consistently achieves optimality gaps below 0.01% and obtains solutions within 3–37 seconds across all test instances. By comparison, CPLEX requires from 12.9 minutes to 2.35 hours for solvable instances and is unable to solve several large-scale scenarios within acceptable computational limits. These findings provide useful insights for planning and decision-making for EVB recycling networks in a low-carbon context.
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Yingfu Zhang, Xuefeng Wang, Mingqiang Yin, Guodong Liang, Xiaohu Qian, Chunxi Han. Green electric vehicle battery recycling network design under supply chain disruptions: a 4PL-based approach with combinatorial auction-based winner determination[J]. Journal of Industrial and Management Optimization, 2026, 22(8): 3562-3604. doi: 10.3934/jimo.2026130
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