Ravelijn

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

Assistant Professor
BMS-HBE-IEBIS
Ravelijn 3351
w.j.a.vanheeswijk@utwente.nl
+31534898460
Business card

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 the  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.

Expertise

  • Social Sciences

    • Logistics
    • Simulation
    • Costs
    • Problem

  • Computer Science

    • Dynamic Programming
    • Reinforcement Learning
    • Function Approximation
    • Models

Organisations

Publications

2025

Strategic bidding in freight transport using deep reinforcement learning (2025)Annals of operations research, 350(1), 131-168. van Heeswijk, W. J. A.https://doi.org/10.1007/s10479-022-04572-zMachine Learning Predictions for Traffic Equilibria in Road Renovation Scheduling (2025)[Working paper › Preprint]. ArXiv.org. Bosch, R., van Heeswijk, W., Rogetzer, P. & Mes, M.https://doi.org/10.48550/arXiv.2506.05933The Stochastic Dynamic Postdisaster Inventory Allocation Problem with Trucks and UAVs (2025)Transportation science, 59(2), 360-390. van Steenbergen, R. M., van Heeswijk, W. J. A. & Mes, M. R. K.https://doi.org/10.1287/trsc.2023.0438

2024

Evaluation and Coordination of UAVs in Humanitarian Logistics (2024)[Thesis › PhD Thesis - Research UT, graduation UT]. University of Twente. van Steenbergen, R. M.https://doi.org/10.3990/1.9789036562232Group Workshop as a “Human-Centered Approach” for Identification and Selection of Business Processes for Robotic Process Automation (2024)In Towards Digital and Sustainable Organisations - People, Platforms, and Ecosystems (pp. 103-122) (Lecture Notes in Information Systems and Organisation; Vol. 65 LNISO). Springer. Berghuis, L., Abhishta, A., van Heeswijk, W. & Tursunbayeva, A.https://doi.org/10.1007/978-3-031-52880-4_7Towards self-organizing logistics in transportation: a literature review and typology (2024)International transactions in operational research, 31(3), 1309-1374. Gerrits, B., van Heeswijk, W. & Mes, M.https://doi.org/10.1111/itor.13408Dynamic Neighborhood Construction for Structured Large Discrete Action Spaces (2024)[Contribution to conference › Paper] 12th International Conference on Learning Representations, ICLR 2024. Akkerman, F., Luy, J., van Heeswijk, W. & Schiffer, M.

2023

Reinforcement learning for humanitarian relief distribution with trucks and UAVs under travel time uncertainty (2023)Transportation 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 Stochastic Dynamic Post-Disaster Inventory Allocation Problem with Trucks and UAVs (2023)[Working paper › Working paper]. ArXiv.org. van Steenbergen, R. M., van Heeswijk, W. J. A. & Mes, M.https://doi.org/10.48550/arXiv.2312.00140The Heterogeneous Fleet Risk-Constrained Vehicle Routing Problem in Humanitarian Logistics (2023)In 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. van Steenbergen, R. M., Lalla-Ruiz, E., van Heeswijk, W. J. A. & Mes, M.https://doi.org/10.1007/978-3-031-43612-3_17

Research profiles

Address

University of Twente

Ravelijn (building no. 10), room 3351
Hallenweg 17
7522 NH Enschede
Netherlands

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