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

Matteo Brunetti, Giovanni Campuzano, Martijn Mes

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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.
Original languageEnglish
Title of host publication2022 Winter Simulation Conference
EditorsB. Feng, G. Pedrielli, Y. Peng, S. Shashaani, E. Song, C.G. Corlu, L.H. Lee, E.P. Chew, T. Roeder, P. Lendermann
Number of pages12
ISBN (Electronic)978-1-6654-7661-4
Publication statusPublished - 23 Jan 2023
Event2022 Winter Simulation Conference, WSC 2022 - Singapore, Singapore
Duration: 11 Dec 202214 Dec 2022

Publication series

NameProceedings - Winter Simulation Conference
ISSN (Print)0891-7736


Conference2022 Winter Simulation Conference, WSC 2022
Abbreviated titleWSC 2022


  • 2023 OA procedure


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