Wouter is an assistant professor in Operations Research and Financial Engineering. His research- and teaching activities center around optimization, simulation and machine learning - in particular reinforcement learning - with a focus on applications in logistics and finance. He teaches a variety of BSc and MSc courses, primarily in the Industrial Engineering & Management (IEM) program, covering topics such as reinforcement learning, option pricing, Markov decision processes and simulation optimization. Additionally, he has supervised over 100 students for their thesis research, and teaches reinforcement learning at the PhD level both nationally and internationally.

His research has been published in outlets such as Transportation Science and the International Conference on Learning Representations (ICLR). He has acquired and is involved in national research projects such as Logiquay (NWA), fMaas (NWA) and DReSC (Dinalog and Health Holland), as well as European ones such as theย  EU COST Action CA19130 Fintech and AI in Finance and the Marie Sklodowska-Curie Action (MSCA) Digital Finance. He supervises a number of PhD candidates in these projects.

For the MSCA Digital Finance project, Wouter is a member of the Executive Board and leads the European training activities. Within the UT, he is chairman of the IEM Program Committee, member of the curriculum team for the IEM satellite program that is presently under development, and chairman of the Reinforcement Learning Network.

Through his activities, Wouter seeks to make an impact on enhancing decision making under uncertainty in an array of corporate and societal problems. By combining machine learning techniques and optimization techniques, novel solution methods can be designed to make a positive impact in terms of performance, risk mitigation, transparency and regulatory compliance. For this, close and ongoing interactions between universities, industry and governmental bodies is essential.


  • Social Sciences

    • Logistics
    • Simulation
    • Costs
    • Problem
    • Urban Areas
    • Freight Transport
  • Computer Science

    • Dynamic Programming
    • Models



Towards self-organizing logistics in transportation: a literature review and typologyInternational transactions in operational research (E-pub ahead of print/First online). Gerrits, B., van Heeswijk, W. & Mes, M.https://doi.org/10.1111/itor.13408Reinforcement learning for humanitarian relief distribution with trucks and UAVs under travel time uncertaintyTransportation Research Part C: Emerging Technologies, 157, Article 104401. van Steenbergen, R. M., Mes, M. & van Heeswijk, W. J. A.https://doi.org/10.1016/j.trc.2023.104401The Heterogeneous Fleet Risk-Constrained Vehicle Routing Problem in Humanitarian LogisticsIn Computational Logistics: 14th International Conference, ICCL 2023, Berlin, Germany, September 6โ€“8, 2023, Proceedings (pp. 276-291). Springer. van Steenbergen, R. M., Lalla-Ruiz, E., van Heeswijk, W. J. A. & Mes, M.https://doi.org/10.1007/978-3-031-43612-3_17Scheduling Urban Infrastructure Renovation Projects to Minimize Traffic DisruptionIn 14th International Conference on Computational Logistics. Bosch, R., Rogetzer, P., van Heeswijk, W. J. A. & Mes, M.Handling Large Discrete Action Spaces via Dynamic Neighborhood Construction. ArXiv.org. Akkerman, F., Luy, J., Heeswijk, W. v. & Schiffer, M.https://arxiv.org/abs/2305.19891A Reference Use Case, Data Space Architecture, and Prototype for Smart Truck ParkingIn Proceedings of the 22nd CIAO! Doctoral Consortium, and Enterprise Engineering Working Conference Forum 2022 co-located with 12th Enterprise Engineering Working Conference (EEWC 2022), Article 1 (pp. 1-15). CEUR. Piest, J. P. S., Slavova, S. & van Heeswijk, W. J. A.https://ceur-ws.org/Vol-3388/paper1.pdfMethodology for Evaluating the Appropriateness of a Business Process for Robotic Process AutomationIn Impact of Artificial Intelligence in Business and Society: Opportunities and Challenges (pp. 105-133). Taylor & Francis. Abhishta, A., Berghuis, L., van Heeswijk, W. & Tursunbayeva, A.https://doi.org/10.4324/9781003304616-8
Deep reinforcement learning in linear discrete action spacesIn Proceedings of the 2020 Winter Simulation Conference, WSC 2020, Article 9384078 (pp. 1063-1074). IEEE. van Heeswijk, W. & La Poutre, H.https://doi.org/10.1109/WSC48552.2020.9384078

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