Simulation of the Internal Electric Fleet Dispatching Problem at a Seaport: A Reinforcement Learning Approach

Through discrete-event simulation, we evaluate the impact of using a fleet of electric and autonomous vehicles (EAVs) to decouple inbound trucks from the internal freight flows in a seaport located in the Netherlands. To support the operational control of EAVs, we use agent-based modeling and support the decision-making capabilities using a reinforcement learning (RL) approach. More specifically, to model the assignment of EAVs to container transport or battery charge, we introduce the Internal Electric Fleet Dispatching Problem (IEFDP). To solve the IEFDP, we propose an RL approach and benchmark its performance against four different assignment heuristics. Our results are compelling: the RL approach outperforms the benchmark heuristics, and the decoupling process significantly reduces congestion and waiting times for truck drivers as well as potentially improve the traffic's sustainability, against a slight increase in length of stay of containers at the port.