Integrated vessel traffic scheduling and berth allocation with restricted channel widths

Abstract

Due to the surging volume of seaborne trade and high frequencies of port calls by vessels, seaports worldwide have been experiencing various levels of traffic congestion in the past few years. The incoming and outgoing vessel traffic in port areas is bottlenecked by the traffic infrastructure (e.g., navigation channels and inner anchorages) and the hydrological conditions (e.g., tidal effects) of a port, which can lead to significant congestion when vessel traffic and vessel service are not effectively planned. In this study, we investigate an integrated vessel traffic scheduling and berth allocation problem for congestion mitigation in a port. The problem encompasses the decision-making process of scheduling incoming and outgoing vessels in the navigation channels and anchorage areas of a port, and allocating berth space to vessels for service, so as to minimize the overall berthing and departure delay of vessels. In particular, we consider a practical scenario where the width of each channel may vary at different segments. This channel width restriction can render the problem much more difficult compared to a traditional setting with identical channel widths. We develop a binary integer programming model for the problem, and present a novel machine-learning-enhanced column generation algorithm for addressing this complex problem. Our method applies machine learning models to restrict vessels’ port stay times within limited time ranges, so that the search space for column generation can be reduced, leading to a trade-off between solution quality and computation efficiency. We validate the effectiveness of the proposed solution method utilizing both a case study of the Port of Shanghai and a computational study on synthetic large-scale instances.