JL
J. Lemut
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The Robustness of the GNN approach in application of different data-generation method
How robust is the GNN approach when applied to instances generated using different data-generation methods or distributions?
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.
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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.