Predict-then-Optimize for Seaport Power-Logistics Scheduling: Generalization across Varying Tasks Stream
This work addresses generalization issues in seaport scheduling for logistics operators, though it is incremental as it builds on existing decision-focused learning methods.
The paper tackles the problem of poor generalization in decision-focused learning for seaport power-logistics scheduling by proposing a decision-focused continual learning framework that adapts online to varying tasks, achieving superior decision performance and reduced computational cost in experiments calibrated to Jurong Port.
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.