dr.ir. W.J.A. van Heeswijk (Wouter)

Engineering & Materials Science

# Autonomous Agents
# Consolidation
# Dynamic Programming
# Logistics
# Supply Chains

Business & Economics

# Consolidation

Mathematics

# Reinforcement Learning
# Serious Games

Faculty of Behavioural, Management and Social Sciences (BMS), Industrial Engineering & Business Information Systems (IEBIS)

van Steenbergen, R. M. , Mes, M. , & van Heeswijk, W. J. A. (2023). Reinforcement learning for humanitarian relief distribution with trucks and UAVs under travel time uncertainty. Transportation Research Part C: Emerging Technologies, 157, Article 104401. Advance online publication. https://doi.org/10.1016/j.trc.2023.104401

Bosch, R. , Rogetzer, P. , van Heeswijk, W. J. A. , & Mes, M. (2023). Scheduling Urban Infrastructure Renovation Projects to Minimize Traffic Disruption. In 14th International Conference on Computational Logistics

van Steenbergen, R. M. , Lalla-Ruiz, E. , van Heeswijk, W. J. A. , & Mes, M. (2023). The Heterogeneous Fleet Risk-Constrained Vehicle Routing Problem in Humanitarian Logistics. In J. R. Daduna, G. Liedtke, X. Shi, & S. Voß (Eds.), Computational Logistics: 14th International Conference, ICCL 2023, Berlin, Germany, September 6–8, 2023, Proceedings (pp. 276-291). (Lecture Notes in Computer Science; Vol. 14239). Springer. https://doi.org/10.1007/978-3-031-43612-3_17

Akkerman, F., Luy, J. , Heeswijk, W. V., & Schiffer, M. (2023). Handling Large Discrete Action Spaces via Dynamic Neighborhood Construction. ArXiv.org. https://arxiv.org/abs/2305.19891

Piest, J. P. S., Slavova, S. , & van Heeswijk, W. J. A. (2023). A Reference Use Case, Data Space Architecture, and Prototype for Smart Truck Parking. In Proceedings of the 22nd CIAO! Doctoral Consortium, and Enterprise Engineering Working Conference Forum 2022 co-located with 12th Enterprise Engineering Working Conference (EEWC 2022) (pp. 1-15). Article 1 (CEUR Workshop Proceedings; Vol. 3388). CEUR. https://ceur-ws.org/Vol-3388/paper1.pdf

van Heeswijk, W. J. A. (2022). Strategic bidding in freight transport using deep reinforcement learning. Annals of operations research. Advance online publication. https://doi.org/10.1007/s10479-022-04572-z

Slavova, S. , Piest, J. P. S. , & van Heeswijk, W. J. A. (2022). Predicting truck parking occupancy using machine learning. Procedia computer science, 201, 40-47. https://doi.org/10.1016/j.procs.2022.03.008

van Heeswijk, W., & La Poutre, H. (2021). Deep reinforcement learning in linear discrete action spaces. In K-H. Bae, B. Feng, S. Kim, S. Lazarova-Molnar, Z. Zheng, T. Roeder, & R. Thiesing (Eds.), Proceedings of the 2020 Winter Simulation Conference, WSC 2020 (pp. 1063-1074). Article 9384078 (Proceedings - Winter Simulation Conference; Vol. 2020). IEEE. https://doi.org/10.1109/WSC48552.2020.9384078

Mes, M. , & van Heeswijk, W. (2020). Comparison of Manual and Automated Decision-Making with a Logistics Serious Game. In E. Lalla-Ruiz, M. Mes, & S. Voß (Eds.), Computational Logistics - 11th International Conference, ICCL 2020, Proceedings (pp. 698-714). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12433 LNCS). Springer. https://doi.org/10.1007/978-3-030-59747-4_45

van Heeswijk, W. (2020). Smart Containers with Bidding Capacity: A Policy Gradient Algorithm for Semi-Cooperative Learning. In E. Lalla-Ruiz, M. Mes, & S. Voß (Eds.), Computational Logistics: 11th International Conference, ICCL 2020, Enschede, The Netherlands, September 28–30, 2020, Proceedings (pp. 52-67). (Lecture notes in computer science; Vol. 12433). Springer. https://doi.org/10.1007/978-3-030-59747-4_4

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