LG
L.H.M. Geurts
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Reinforcement Learning for the Dynamic Berth Allocation Problem
Evaluating the Robustness of a Graph Neural Network Agent under Uncertainty of Estimated Time of Arrival
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. ...
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. ...
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.
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.