Adaptive large neighborhood search algorithm for redesigning a closed-loop supply chain network considering capacity configurations

Redesigning a closed-loop supply chain (CLSC) network is essential for enhancing efficiency and resilience in high-growth industries such as the tire industry. A CLSC integrates forward and reverse flows, to create a complex network structure. This study formulates a mixed-integer programming model to support strategic and tactical decisions, including facility openings and closures and capacity reconfiguration, while ensuring balanced demand flow. To solve this complex network design problem efficiently, an adaptive large neighborhood search (ALNS) algorithm is developed, incorporating customized destruction–construction operators and linear relaxation. The algorithm’s performance is evaluated on 64 test instances of varying sizes and compared with an exact solver. Results show that the ALNS achieves optimal solutions for small to medium-sized cases and maintains average optimality gaps around 10% with high computational efficiency. Sensitivity analyses highlight the significant influence of redesign costs, capacity flexibility, and market demand on network structure and total cost. From a managerial perspective, the findings underscore the importance of investing in capacity expansion and flexible infrastructure to achieve a cost-effective resilient CLSC network design.