Optimizing Berth Allocation in Maritime Transportation with Quay Crane Setup Times Using Reinforcement Learning

Abstract

Maritime transportation plays a critical role in global trade as it accounts for over 80% of all merchandise movement. Given the growing volume of maritime freight, it is vital to have an efficient system for handling ships and cargos at ports. The current first-come-first-serve method is insufficient in maintaining operational efficiency, especially under complicated conditions such as parallel scheduling with different cargo setups. In addition, in the face of rising demand, data-driven strategies are necessary. To tackle this issue, this paper proposes a mixed-integer model for allocating quay cranes, terminals, and berths. It considers not only cargo types, but also the time required for a quay crane setup. The proposed model features a greedy-insert-based offline algorithm that optimizes berth allocation when vessel information is available. In situations where vessel information is uncertain, the model utilizes an online optimization strategy based on a reinforcement-learning algorithm that is capable of learning from feedback and of adapting quickly in real time. The results of the numerical experiments demonstrate that both the offline and online algorithms can significantly enhance cargo handling efficiency and overall harbor operation. Furthermore, they have the potential to be extended to other complex settings.

A survey on computational intelligence approaches for intelligent marine terminal operations

“…From the presented simulation results it becomes evident that the proposed model can help in achieving efficient berth and quay crane allocation. The authors of [117] propose a greedy insert-based offline model to optimize BAP when vessel information is available. They further propose an online strategy based on a reinforcement-learning algorithm to solve the problem when vessel information is uncertain.…”