TK
T. Kuklys
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Reinforcement Learning for the Discrete Dynamic Berth Allocation Problem
Evaluating a trained Discrete Dynamic Berth Allocation model on Berth breakdowns
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.
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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.