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

2025-11-11

AI Summary

In modern port electric logistics scheduling, the dynamic arrival of vessels undermines the generalizability and task adaptability of conventional prediction-optimization paradigms. Method: This paper proposes a decision-oriented continual learning framework that jointly integrates Fisher information matrix regularization with a differentiable convex surrogate optimization model. The framework enables end-to-end co-optimization of prediction and scheduling decisions, ensuring memory stability over historical tasks while supporting online adaptation to newly arriving vessel streams. Contribution/Results: Compared to existing decision-oriented learning approaches, our method significantly improves cross-task generalization and decision quality while reducing long-term computational overhead. Empirical evaluation on real-world operations at Jurong Port demonstrates superior scheduling performance over state-of-the-art methods, validating both its theoretical innovation and engineering practicality.

Technology Category

• Planning, Routing, and Scheduling: Planning/Scheduling and Learning
• Search and Optimization: Learning to Search
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• Enhancing generalization of port power-logistics scheduling across evolving vessel arrival tasks
• Overcoming task-specific limitations in decision-focused learning for seaport operations
• Developing continual learning framework for sustainable optimization under varying scheduling demands

Innovation

• Decision-focused continual learning framework adapts online
• Fisher information regularization preserves critical prior parameters
• Differentiable convex surrogate stabilizes gradient backpropagation process