The Dynamic Berth Allocation Problem (DBAP) is an optimization problem in maritime logistics that seeks to minimize vessel delays and improve terminal efficiency through effective berth scheduling. This paper investigates how replacing the Graph Neural Network (GNN) encoder in a reinforcement learning approach to the DBAP with a Global-Local Attention mechanism affects the optimality gap. This new encoder architecture is designed to capture both local vessel-berth interactions and global scheduling information. The performance of the proposed architecture is evaluated against the baseline GNN across 2,700 benchmark instances spanning various terminal sizes and traffic congestion levels. Empirical results demonstrate that the Global-Local Attention variant yields a consistent improvement in schedule quality, reducing normalized operational costs by an average of 2.8%. While this modification provides notable optimization gains, particularly in lower congestion scenarios, it introduces an 81.4% increase in trained parameters, presenting a distinct trade-off between scheduling optimality and computational efficiency. ... Read more

The Dynamic Berth Allocation Problem is a port
scheduling problem where vessels arrive dynamically over time and must be assigned to a berth. A pre-trained Graph Neural Network (GNN) based
reinforcement learning approach solves the problem efficiently but is dependent on estimated times of arrival [11]. However, vessels predominantly arrive later than estimated. this introduces unwanted uncertainty. We evaluate a pre-trained GNN agent under controlled ETA perturbations to create an information gap between the estimated and actual arrival times, using the original full information setting as a reference. The agent is evaluated using a factorial experimental setup over different deviation levels and instances to evaluate robustness under ETA uncertainty. The results show that the agent is robust to optimistic ETA deviations, with only limited performance degradation even at
larger introduced deviations. The robustness appears to mainly stem from the reactive nature of the agent and its associated scheduling process, which
limits the influence of ETA deviations on the decision making process. ... Read more

This study looks at whether we can replace the Graph Neural Network (GNN)
encoder in a reinforcement learning framework with a direct attention mechanism to solve the Dynamic Discrete Berth Allocation Problem (DDBAP): the challenge of assigning ships to specific docking spots over time. We tested two alternative designs, a Pure Attention Transformer and a topology-aware Edge-Transformer, against a baseline Graph Attention Network (GAT) using 27 simulated shipping scenarios. On average, the baseline GNN performed relatively better overall, scoring an average cost of 646.58 compared to 650.72 for the Edge-Transformer and 658.63 for the Pure Transformer. However, the best choice depends entirely on the size of the problem. The GNN works best in low-traffic situations with plenty of available berths because it ignores irrelevant background information. In contrast, when the port gets highly congested, such as 120 ships competing for just 5 berths, the global attention mechanism performs much better because it can anticipate long-term queue delays. Finally, while the attention-based models take significantly longer to train over 30,000 samples, they both process decisions in less than a second during live testing. This makes them highly practical for real-time maritime scheduling. ... Read more

Sea cargo transportation relies on the efficiency of ports and how effectively ships are allocated to berths. This study evaluates the robustness of the Graph Neural Network proposed by Moya et. al. [7] for the Berth Allocation Problem. It uses real world scenarios and known cases to scrutinize the agent in comparison to common heuristics and a baseline optimal solution. While the agent created schedules that were in general on par with the baseline heuristics, the agent performed particularly poorly in changes to the handling times variable. ... Read more

The problem of scheduling vessels in a port as they arrive one-by-one is known as the Dynamic Berth Allocation Problem and it is NP-hard. This paper analyses the influence of berth breakdowns on the scheduling and optimality of a trained Reinforcement Learning model by March Moya et al. for such a problem. Several breakdown parameters, including frequency, severity, dura- tion, and the probability of binary versus partial breakdowns, were examined independently and in combination with one another with respect to different scheduling heuristics. The breakdowns were dynamically injected into the model’s event loop so that it did not have knowledge of upcoming breakdowns. Each experimental configuration was evaluated using ten random seeds and the sample mean and standard deviation were computed. The results showed low variance between different seeds and configurations. Breakdown frequency was the main factor limiting the model’s perfor- mance, moving the performance from a 19.6% advantage to 6.4% in the most extreme cases when compared to the baseline heuristic of WTSP. The other parameters did not produce significant model degradation. ... Read more