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Jesse van Remmerden
PhD candidate at Eindhoven University of Technology working on reinforcement learning, discrete diffusion and language models for combinatorial optimization.
Posts
publications
A Deep Multi-Objective Reinforcement Learning Approach for Infrastructural Maintenance Planning with Non-Linear Utility Functions
Published in Multi-Objective Decision Making Workshop (MODeM) at ECAI 2023, Kraków, 2023
The workshop paper that introduced the multi-objective actor–critic approach to infrastructural maintenance planning later developed into MO-DCMAC. Presented at the Multi-Objective Decision Making (MODeM) workshop at ECAI 2023 in Kraków.
Recommended citation: van Remmerden, J., Kenter, M., Roijers, D. M., Zhang, Y., & Andriotis, C. (2023). A Deep Multi-Objective Reinforcement Learning Approach for Infrastructural Maintenance Planning with Non-Linear Utility Functions. Multi-Objective Decision Making Workshop (MODeM) at ECAI 2023, Kraków, Poland.
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Deep multi-objective reinforcement learning for utility-based infrastructural maintenance optimization
Published in Neural Computing and Applications, 2025
MO-DCMAC is the first deep multi-objective RL method of its kind applied to a real-world infrastructure problem. Rather than collapsing cost and probability of collapse into a shaped reward, it optimizes directly over non-linear utilities — including FMECA, the risk-scoring methodology Amsterdam’s asset managers already use in practice. It produced the best maintenance plan in 5 of 6 test settings, improving on the rule-based strategies currently in use by up to 46%.
Recommended citation: van Remmerden, J., Kenter, M., Roijers, D. M., Andriotis, C., Zhang, Y., & Bukhsh, Z. (2025). Deep multi-objective reinforcement learning for utility-based infrastructural maintenance optimization. Neural Computing and Applications. https://doi.org/10.1007/s00521-024-10954-0
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Offline Reinforcement Learning for Learning to Dispatch for Job Shop Scheduling
Published in Machine Learning, 2025
Offline Learned Dispatching (Offline-LD) is the first fully end-to-end offline RL approach for the Job Shop Scheduling Problem, learning dispatching policies from historical schedules with no simulator at all. Trained on only 100 constraint-programming solutions, it outperforms online RL baselines that require millions of simulated interactions — and performs no worse when that training data is deliberately corrupted with noise.
Recommended citation: van Remmerden, J., Bukhsh, Z., & Zhang, Y. (2025). Offline reinforcement learning for learning to dispatch for job shop scheduling. Machine Learning, 114(8), 191. https://doi.org/10.1007/s10994-025-06826-w
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Generalizing Beyond Suboptimality: Offline Reinforcement Learning Learns Effective Scheduling through Random Solutions
Published in Transactions on Machine Learning Research, 2026
CDQAC is an offline RL algorithm for job shop and flexible job shop scheduling that learns effective policies from historical data without any simulator. It matches or beats state-of-the-art online and offline RL baselines from only 10–25 training instances, generalizes to instances ten times larger than those seen in training, and performs best when trained on solutions generated by a random heuristic.
Recommended citation: van Remmerden, J., Bukhsh, Z., & Zhang, Y. (2026). Generalizing Beyond Suboptimality: Offline Reinforcement Learning Learns Effective Scheduling through Random Solutions. Transactions on Machine Learning Research.
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DROVER: Diffusion-Based Routing with Verbal Preferences
Published in Under review, 2026
A language-conditioned discrete diffusion model for vehicle routing that takes its constraints as natural-language requests, together with the first benchmark and automatic verifier for this setting.
Recommended citation: van Remmerden, J., Wu, Y., Zhang, Y., & Bukhsh, Z. DROVER: Diffusion-Based Routing with Verbal Preferences. Under review.
talks
Workshop presentation during MODeM 2023
Published:
At the Multi-Objective Decision Making (MODeM) workshop, co-located with ECAI 2023 in Kraków, I presented my workshop paper on multi-objective reinforcement learning for infrastructural maintenance planning.
Oral presentation during ICT Open 2024
Published:
I gave an oral presentation at NWO ICT.OPEN 2024 on utility-based multi-objective reinforcement learning for infrastructural maintenance planning.
Poster Session during DLRL Summer School 2024
Published:
At the Deep Learning and Reinforcement Learning (DLRL) Summer School 2024 in Toronto, hosted by CIFAR, Amii, Mila and the Vector Institute, I presented my research on multi-objective reinforcement learning for infrastructural maintenance planning in the poster session.
Offline Reinforcement Learning for Learning to Dispatch for Job Shop Scheduling at ECML PKDD 2025
Published:
I presented Offline-LD in the Journal Track of ECML PKDD 2025, the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, held in Porto from 15–19 September 2025. The paper appeared in Machine Learning (Springer) and was selected for presentation at the conference through the journal track.
Generalizing Beyond Suboptimality: Offline RL Learns Effective Scheduling Through Random Solutions
Published:
I presented CDQAC in the Smart Industry track at NWO ICT.OPEN 2026, held at De Doelen in Rotterdam on 15–16 April 2026 under the theme The Art of ICT. The track was chaired by Jurjen Helmus (HvA) and John Bolte (HHS).
Tutorial: Deep Reinforcement Learning for Combinatorial Optimization at IJCAI-ECAI 2026
Published:
I was one of five speakers on the half-day tutorial “Deep Reinforcement Learning for Combinatorial Optimization” (T17) at IJCAI-ECAI 2026, the joint International Joint Conference on Artificial Intelligence and European Conference on Artificial Intelligence, held in Bremen from 15–21 August 2026.
DROVER: Diffusion-Based Routing with Verbal Preferences
Published:
I presented DROVER in Session 1 of the Data Science Meets Optimisation (DSO) workshop at IJCAI-ECAI 2026, held in Bremen on 16 August 2026. The workshop runs under the auspices of the DSO working group of the Association of European Operational Research Societies (EURO), and was chaired by Yaoxin Wu (TU/e), Jayanta Mandi (KU Leuven), Neil Yorke-Smith (TU Delft) and Yingqian Zhang (TU/e). The work is joint with Zaharah Bukhsh and Yingqian Zhang.