Simultaneous optimization for berth and quay crane scheduling in container terminals

Abstract

The rising growth of the global economy along with maritime trade prosperity highlight the resilience of this industry despite challenges. Seaports, as prominent hubs of the international supply chain, handle approximately 80 % of global freight volume which necessitates the efficient planning and scheduling of operations. This study addresses the functional and deep integrated planning of the berth allocation problem (BAP), quay crane assignment problem (QCAP), and quay crane scheduling problem (QCSP). Also, a novel algorithm for scheduling the QCs within the deep integrated scheme was developed. A comprehensive comparison of integration approaches for the three problems’ MILP model was proposed utilizing hybrid solution algorithms with Particle Swarm Optimization (PSO) and Dynamic Programming (DP) methods. The objective functions aimed at minimizing the costs of berth position deviation from the best berth location, deviation of end time from departure time, costs of services during berth time, and the setup number of QCs. Findings demonstrated that the deep integrated model with developed QCSP algorithm and classic PSO solution demonstrates superior cost optimization compared to hybrid methods which prioritize computational efficiency at the expense of solution quality, particularly in large-scale scenarios. The trade-off between solution quality and runtime of the classic PSO, underscores the algorithm's strengths for offline optimization and significant cost savings in strategic planning but highlights challenges in dynamic, real-time applications. The effectiveness and practicality of the proposed algorithms were also validated and future research suggestions provided for more developments.