Faculty & Research
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Gijsbrechts, Joren
Associate Professor, Operations, Innovation and Technology Management (OITM) at Esade
URL Contracted Doctoral Professor
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 assistant professor in Operations Management.
Prior to obtaining his doctoral degree from KU Leuven in Belgium, he attained business experience in the Supply Chain and Operations division of Procter and Gamble in Sweden.
His research primarily focuses on data-driven decision making in Operations Management, with a particular interest in the recent advancements in Machine Learning and Prescriptive Analytics.
Currently, he is working on combining Machine Learning, Robust Optimization and large-scale simulation techniques to develop data-driven algorithms.
His models have assisted companies from the telecommunications and consumer goods industry to improve their decision making.
In addition to research, he is providing guest lectures and company workshops on the recent developments within Business.
Selected publications
- 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.
- 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.