Faculty & Research
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Gijsbrechts, Joren
Education
- PhD in Operations Management. KU Keuven
- Master in Business Engineering. University of Antwerp
- Bachelor un Business Engineering. University of Antwerp
Biography
Joren Gijsbrechts is an Associate Professor of Operations Management at Esade Business School. Before earning his Ph.D. from KU Leuven in 2020, he gained industry experience in the Supply Chain and Operations division of Procter & Gamble in Sweden, and he has since held visiting positions at Kellogg School of Management and MIT Sloan.
His research designs algorithms that turn complex datasets into actionable operational decisions, combining deep reinforcement learning, robust optimization, and large-scale simulation. Recent work has appeared in Management Science, Manufacturing & Service Operations Management, Production and Operations Management, and the Proceedings of the International Conference on Machine Learning, addressing problems ranging from dual sourcing and reshoring to inventory control in multi-echelon networks and the energy footprint of agentic AI workflows.
His models have helped firms in consumer goods, telecommunications, and online retail improve their decision making. Alongside his research, he delivers tailored talks and executive workshops on AI, analytics, and Supply Chain 4.0.
Selected publications
- De Moor, B. J., Boute, R. N., Creemers, S. & Gijsbrechts, J. (E-pub ahead of print). (2026). Discount replenishment opportunities with limited capacity: Optimal policy and pricing dynamics. European Journal of Operational Research. DOI: https://doi.org/10.1016/j.ejor.2026.04.016.
- Gijsbrechts, J., Boute, R. N., Mieghem, J. A. & Zhang, D. J. (2026). AI in Inventory Management. Springer Series in Supply Chain Management (pp. 137-148). Springer Nature. DOI: https://doi.org/10.1007/978-3-032-07054-8_10.
- Farias, V., Gijsbrechts, J., Khojandi, A., Peng, T. & Zheng, A. (2025). Speeding Up Policy Simulation in Supply Chain RL. Proceedings of Machine Learning Research, 267, pp. 16161-16177. DOI: https://doi.org/10.48550/arXiv.2406.01939.
- Gijsbrechts, J. & Van Staden, H. E. (2025). Where Did the Demand Go? Teaching Demand Censoring with the Newsvendor Challenge. INFORMS Transactions on Education, 25 (3), pp. 257-264. DOI: https://doi.org/10.1287/ited.2024.0085.
- Gijsbrechts, J., Boute, R. N., Disney, S. M. & Van Mieghem, J. A. (2024). Volume flexibility at responsive suppliers in reshoring decisions: Analysis of a dual sourcing inventory model. Production and Operations Management, 34 (2), pp. 271-302. DOI: https://doi.org/10.1111/poms.13719.
- Fišar, M., Greiner, B., Huber, C., Katok, E., Ozkes, A. I., Abraham, D., Adams, G. S., Adbi, A., Addoum, J. M., Adena, M., Akella, L. Y., Akey, P., Akmansoy, O., Alban, A., Alexeev, V., Alimov, A., Aman, A., Aouad, A., Appel, G., Arnosti, N., Arora, K., ... (2024). Reproducibility in Management Science. Management Science, 70 (3), pp. 1343-1356. DOI: https://doi.org/10.1287/mnsc.2023.03556.
- Gijsbrechts, J., Imdahl, C., Boute, R. N. & Van Mieghem, J. A. (2023). Optimal robust inventory management with volume flexibility: Matching capacity and demand with the lookahead peak-shaving policy. Production and Operations Management, 32 (11), pp. 3357-3373. DOI: https://doi.org/10.1111/poms.14069.
- De Moor, B. J., Gijsbrechts, J. & Boute, R. N. (2022). Reward shaping to improve the performance of deep reinforcement learning in perishable inventory management. European Journal of Operational Research, 301 (2), pp. 535-545. DOI: https://doi.org/10.1016/j.ejor.2021.10.045.
- Gijsbrechts, J., Boute, R. N., Van Mieghem, J. A. & Zhang, D. (2022). Can Deep Reinforcement Learning Improve Inventory Management?: Performance on Lost Sales, Dual-Sourcing, and Multi-Echelon Problems. Manufacturing & Service Operations Management, 24 (3), pp. 1349-1368. DOI: https://doi.org/10.1287/msom.2021.1064.
- Boute, R. N., Gijsbrechts, J., van Jaarsveld, W. & Vanvuchelen, N. (2022, April). Deep reinforcement learning for inventory control: A roadmap. European Journal of Operational Research, 298 (2), pp. 401-412. DOI: https://doi.org/10.1016/j.ejor.2021.07.016.
- Boute, R. N., Disney, S. M., Gijsbrechts, J. & Van Miegheme, J. A. (2022). Dual Sourcing and Smoothing Under Nonstationary Demand Time Series: Reshoring with SpeedFactories. Management Science, 68 (2), pp. 1039-1057. DOI: https://doi.org/10.1287/mnsc.2020.3951.
- Boute, R. N., Gijsbrechts, J. & Van Mieghem, J. A. (2022). Digital Lean Operations. Springer Series in Supply Chain Management (pp. 175-188). Springer Nature. DOI: https://doi.org/10.1007/978-3-030-75729-8_6.
- Yee, H., Gijsbrechts, J. & Boute, R. N. (2021). Synchromodal transportation planning using travel time information. Computers in Industry, 125, 103367. DOI: https://doi.org/10.1016/j.compind.2020.103367.
- Vanvuchelen, N., Gijsbrechts, J. & Boute, R. N. (2020). Use of Proximal Policy Optimization for the Joint Replenishment Problem. Computers in Industry, 119, 103239. DOI: https://doi.org/10.1016/j.compind.2020.103239.
- Lemmens, N., Gijsbrechts, J. & Boute, R. N. (2019). Synchromodality in the Physical Internet ¿ dual sourcing and real-time switching between transport modes. European Transport Research Review, 11 (1), 19. DOI: https://doi.org/10.1186/s12544-019-0357-5.