A Reinforcement Learning Approach for the Dynamic Berth Allocation Problem

Abstract

Efficiently assigning vessels to berths in a busy port is a hard scheduling problem: decisions must be made in real time, under uncertainty about future arrivals, and with the goal of minimizing how long ships spend in the port. We present a learning-based approach in which an agent is trained, purely through trial and error, to perform these assignments one at a time as the vessels arrive. The agent represents the current port state as a graph, capturing relationships between waiting vessels, available berths, and ships still on their way, and learns which assignments are promising via graph-based attention. Our formulation incorporates vessel priorities, berth-dependent handling times, and a skip action that allows the agent to postpone assignments when beneficial. Across a range of port congestion levels, the trained agent outperforms classical heuristic rules, achieving on average a 20% reduction in priority-weighted turnaround cost relative to the best heuristic baseline.