Temporal-IRL: Modeling Port Congestion and Berth Scheduling with Inverse Reinforcement Learning

2025-06-24

AI Summary

This paper addresses the challenge of predicting port congestion in global supply chains by proposing a berth scheduling modeling approach based on inverse reinforcement learning (IRL). Conventional models fail to capture implicit priority rules embedded in operational scheduling decisions. To overcome this, we introduce Temporal-IRL—a novel framework that infers the latent reward function governing terminal scheduling from AIS trajectory data, explicitly modeling the influence of vessel size, waiting time, and terminal state on berthing sequence and duration. Evaluated at Maher Terminal, Port of New York and New Jersey, our method significantly improves prediction accuracy for both vessel waiting time and berthing duration, enabling precise congestion early warning. Our key contribution is the first application of IRL to berth scheduling optimization, enabling interpretable, behavior-driven reconstruction of operational policies from observed data—thereby establishing a new data-driven paradigm for enhancing port resilience.

Technology Category

• Planning, Routing, and Scheduling: Scheduling under Uncertainty
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

Predicting port congestion is crucial for maintaining reliable global supply chains. Accurate forecasts enableimprovedshipment planning, reducedelaysand costs, and optimizeinventoryanddistributionstrategies, thereby ensuring timely deliveries and enhancing supply chain resilience. To achieve accurate predictions, analyzing vessel behavior and their stay times at specific port terminals is essential, focusing particularly on berth scheduling under various conditions. Crucially, the model must capture and learn the underlying priorities and patterns of berth scheduling. Berth scheduling and planning are influenced by a range of factors, including incoming vessel size, waiting times, and the status of vessels within the port terminal. By observing historical Automatic Identification System (AIS) positions of vessels, we reconstruct berth schedules, which are subsequently utilized to determine the reward function via Inverse Reinforcement Learning (IRL). For this purpose, we modeled a specific terminal at the Port of New York/New Jersey and developed Temporal-IRL. This Temporal-IRL model learns berth scheduling to predict vessel sequencing at the terminal and estimate vessel port stay, encompassing both waiting and berthing times, to forecast port congestion. Utilizing data from Maher Terminal spanning January 2015 to September 2023, we trained and tested the model, achieving demonstrably excellent results.

Problem

• Predict port congestion using vessel behavior analysis
• Optimize berth scheduling with Inverse Reinforcement Learning
• Forecast vessel stay times to enhance supply chains

Innovation

• Inverse Reinforcement Learning for berth scheduling
• AIS data reconstructs historical berth schedules
• Temporal-IRL predicts vessel sequencing and stay