An integrated framework of vessel demand shifting and port capacity utilization for congestion mitigation

Port congestion remains a critical hurdle for global maritime logistics, particularly as opportunities for infrastructure expansion become increasingly limited. In this study, we introduce a unified optimization framework that coordinates vessel service demand with port capacity, empowering the mitigation of port congestion. The integrated framework comprises three key components: A demand shifting model that optimizes vessel arrival distributions over time; a descriptive queueing model that characterizes vessel arrival and service processes, enabling a holistic evaluation of congestion levels; and a resource management model that effectively allocates available port resources to vessels by leveraging a data-driven throughput envelope calibrated from port operational data. To address the computational challenges posed by the integration these models, we develop a bi-level iterative solution algorithm that iteratively enhances the coordination between demand-side and supply-side decisions, achieving a satisfactory performance with respect to tractability and scalability. Empirical validation is performed using a large-scale, real-world operational dataset from a major container port in Shanghai. Our algorithm proves highly scalable, solving large-scale instances 20–30 times faster than Gurobi and achieving near-optimal solutions (e.g., optimality gaps as low as 0.91 %). The framework delivers economic value with a substantial reduction of total system costs (ranging from 14.47 %-16.67 %) over a non-integrated baseline. The results highlight the practical value and generalizability of the unified approach for enhancing the efficiency and resilience of port systems.