Machine Learning Applications for Seaside Port Operations Planning and Scheduling

The surge in containerised trade has intensified the need for efficient resource allocation in port operations, particularly in berth allocation (BAP), quay crane assignment (QCAP) and quay crane scheduling (QCSP) problems. While mathematical programming and metaheuristic approaches have traditionally been used to solve these problems, their scalability and adaptability remain limited. Recent advances in Machine Learning (ML) offer new optimisation possibilities. This paper conducts a systematic literature review of approaches that apply machine learning techniques to seaside port operations (BAP, QCAP, QCSP, and their integrated variants). Operations such as quayside transport planning, landside or yard activities are outside the scope of this review. The review was conducted following PRISMA guidelines and was based on publications indexed in Scopus and Web of Science. The selected works are analysed and classified across nine dimensions. The results highlight a predominant focus on economic optimisation, with BAP being the most frequently addressed problem. Earlier studies broadly applied regression techniques and predictive approaches. At the same time, more recent research has shifted towards deep reinforcement learning and prescriptive solutions, opening new opportunities for sustainable and adaptive port operations. Finally, it has identified key trends and opportunities in ML applications for port operations.