My research

The growing availability of individual-specific data has led to an exciting era of personalized decision-making: A paradigm that exploits the heterogeneity within populations to deliver tailored service decisions and achieve improved outcomes. This approach necessitates learning from data to determine optimal actions and their timing, dynamically adapting to the evolving conditions of each individual (e.g., a patient requiring treatment or a high-tech system requiring maintenance). 

My research focuses on advancing personalized decision-making by leveraging tools from operations research, statistics, and machine learning, with applications in condition-based maintenance of high-tech systems and healthcare operations. 

Publications

1. Drent, C., Drent, M., Arts, J.J. (2024). Condition-based production for stochastically deteriorating systems: Optimal policies and learning. Manufacturing & Service Operations Management, 26(3), 797–1187.

2. Drent, C., Drent, M., van Houtum, G.J. (2024). Optimal data pooling for shared learning in maintenance operations. Operations Research Letters, 52, 107056.

3. Drent, C., Drent, M., Arts, J.J., Kapodistria, S. (2023). Real-time integrated learning and decision making for cumulative shock degradation. Manufacturing & Service Operations Management, 25(1), 235–253.

   • The degradation data set of the corresponding case study can be downloaded here.

4. Drent, C., Kapodistria, S., Boxma, O. (2020). Censored life-time learning: Optimal Bayesian age-replacement policies. Operations Research Letters, 48(6), 827–834. 

5. Drent, C., Olde Keizer, M., van Houtum, G.J. (2020). Dynamic dispatching and repositioning policies for fast-response service networks. European Journal of Operational Research, 285(2), 583–598. 

6. Drent, C., Kapodistria, S., Resing, J.A.C. (2019). Condition-based maintenance policies under imperfect maintenance at scheduled and unscheduled opportunities. Queueing Systems, 93(3-4), 269–308. 

Working papers

• Integrated learning and control for critical systems, with Stella Kapodistria. Major Revision at Production and Operations Management.

• Dedicated maintenance and repair shop control for spare parts networks, with Chaaben Kouki, Melvin Drent, and Mohamed-Zied Babai. Submitted. 

• Real-time integrated learning and decision-making for asset networks, with Peter Verleijsdonk, Stella Kapodistria, and Willem van Jaarsveld. Submitted. 

• Using Markov decision processes to support early identification of treatment response, with Fang Li, Melvin Drent, Maarten Merkx, Niels Kakes, Frederike Jörg, and Talitha Feenstra. Submitted.  

Theses

• Drent, C. (2022). Structured learning and decision making for maintenance. PhD thesis, Eindhoven University of Technology.

  • Media coverage: TechXplore, TU/e, De Ingenieur (in Dutch), Industrial Maintenance (in Dutch).  

  • Winner of the 2023 Willem R. van Zwet Award (best Dutch PhD thesis in statistics or operations research in 2022; see VVSOR and TU/e).

  • Winner of the 2023 Beta PhD Award (best PhD thesis in the Beta Research School in 2021 and 2022; see TU/e).

• Drent, C. (2017). Dynamic dispatching and repositioning policies in service logistics networks. MSc thesis, Eindhoven University of Technology.

Professional publications

• Drent, C., Drent, M. (2023). Leren van het optimale onderhoudsmoment (in Dutch). STAtOR, 24(3-4), 12-16.