| dc.description.abstract | This thesis focuses on the issue of optimizing vessel scheduling at Port Rotterdam, a
significant hub for global marine logistics. Due to the increasing volume of traffic and
complexity of operations, effective management solutions are needed to ensure efficient
resource usage and prevent congestion. This study forecasts the vessel's Estimated Time
of Arrival (ETA) using machine learning, specifically neural networks, to enhance berth
allocation. Through the combination of simulation-based optimization and predictive
analytics, the study provides a robust scheduling system. This framework aims to
improve the operational sustainability and efficiency by decreasing waiting times, berth
location fluctuations, and associated costs. The findings indicate that because the
Priority (PRI) rule balances cost and service effectiveness, it is a feasible substitute for
actual implementation. | en_US |
