Joint Optimization of Multi-Period Empty Container Repositioning and Inventory Control Based on Adaptive Particle Swarm Algorithm

This paper proposes a combined optimization method for multi-period empty container repositioning and inventory control based on adaptive particle swarm optimization (APSO) algorithm, which addresses the limitations of existing research, such as decoupling empty container repositioning and inventory control optimization, and lacking multi-period dynamic collaboration mechanisms. Firstly, a joint optimization model integrating (s, S) inventory control strategy is constructed. By adopting the strategy, the selection of repositioning paths and inventory resource allocation are synergistically optimized to balance unit empty container rental costs, inventory costs, and repositioning costs. Secondly, we design an adaptive particle swarm optimization algorithm, introduce dynamic inertia weight and acceleration coefficient adjustment mechanisms, and design heuristic rules for empty container repositioning. In this way, we reduce unreasonable empty container mobilization through the setting of surplus, shortage, and balance ports of empty containers, which can narrow the search space and improve the algorithm’s global search ability and convergence efficiency in high-dimensional decision spaces. Numerical experiments show that the joint optimization model designed can reduce the total cost of empty container management for shipping companies and maintain the rental cost in a stable state. Sensitivity analysis reveals that the unit container rental cost and the maximum inventory capacity of the port have a significant impact on the total system cost, providing a new approach for shipping companies to reduce empty container management costs.