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

TL;DR

Temporal-IRL is a novel approach that uses inverse reinforcement learning to model berth scheduling and predict port congestion by analyzing vessel behavior and stay times, improving supply chain reliability.

Contribution

This paper introduces Temporal-IRL, a new IRL-based model for accurately predicting vessel sequencing and port congestion from AIS data at a major port terminal.

Findings

Achieved high accuracy in vessel sequencing prediction

Effectively estimated vessel port stay times

Demonstrated model's robustness over multiple years of data

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.