Predict-then-Optimize for Seaport Power-Logistics Scheduling: Generalization across Varying Tasks Stream

• URL: http://arxiv.org/abs/2511.07938v2
• Date: Fri, 14 Nov 2025 01:24:11 GMT
• Title: Predict-then-Optimize for Seaport Power-Logistics Scheduling: Generalization across Varying Tasks Stream
• Authors: Chuanqing Pu, Feilong Fan, Nengling Tai, Yan Xu, Wentao Huang, Honglin Wen,
• Abstract summary: Power-logistics scheduling in modern seaports typically follow a predict-then-optimize pipeline.<n>Decision-focused learning has been proposed, which aligns the training of forecasting models with downstream decision outcomes.<n>We address this gap with a decision-focused continual learning framework that adapts online to a stream of scheduling tasks.
• Score: 5.75432978300135
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Power-logistics scheduling in modern seaports typically follow a predict-then-optimize pipeline. To enhance the decision quality of forecasts, decision-focused learning has been proposed, which aligns the training of forecasting models with downstream decision outcomes. However, this end-to-end design inherently restricts the value of forecasting models to only a specific task structure, and thus generalize poorly to evolving tasks induced by varying seaport vessel arrivals. We address this gap with a decision-focused continual learning framework that adapts online to a stream of scheduling tasks. Specifically, we introduce Fisher information based regularization to enhance cross-task generalization by preserving parameters critical to prior tasks. A differentiable convex surrogate is also developed to stabilize gradient backpropagation. The proposed approach enables learning a decision-aligned forecasting model across a varying tasks stream with a sustainable long-term computational burden. Experiments calibrated to the Jurong Port demonstrate superior decision performance and generalization over existing methods with reduced computational cost.

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