Kim Van Den Houten, Mathijs De Weerdt, David M.J. Tax, Esteban Freydell, Eva Christoupoulou, Alessandro Nati (2023), Rolling-Horizon Simulation Optimization For A Multi-Objective Biomanufacturing Scheduling Problem, In Proceedings of the 2023 Winter Simulation Conference (WSC) p.1912-1923, IEEE.

G. Veviurko, J.W. Böhmer, M.M. de Weerdt (2023), You Shall not Pass: the Zero-Gradient Problem in Predict and Optimize for Convex Optimization.

Junhan Wen, Thomas Abeel, Mathijs de Weerdt (2023), “How sweet are your strawberries?”: Predicting sugariness using non-destructive and affordable hardware, In Frontiers in Plant Science Volume 14.

G. Neustroev, Sytze P.E. Andringa, Remco A. Verzijlbergh, Mathijs M. de Weerdt (2022), Deep Reinforcement Learning for Active Wake Control, In International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2022 p.944-953, International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS).

J.G.M. van der Linden, M.M. de Weerdt, E. Demirović (2022), Fair and Optimal Decision Trees: A Dynamic Programming Approach, S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, A. Oh (Eds.), In Advances in Neural Information Processing Systems 35 (NeurIPS 2022) p.38899-38911, Curran Associates, Inc..

J. Wu, Rob Everhardt, K. Stepanovic, M.M. de Weerdt (2022), GridPenguin: A District Heating Network Simulator, Christopher Gradwohl, Alexandra Degold, Thomas Kienberger (Eds.), In Conference Proceedings New Energy for Industry 2022: 2nd Conference of the Innovation Network, October 13-14, 2022 in Linz, Austria p.132-141, NEFI: New Energy for Industry.

Madeleine E.M.A. van Hövell, Rob M.P. Goverde, Nikola Bešinović, Mathijs M. de Weerdt (2022), Increasing the effectiveness of the capacity usage at rolling stock service locations, In Journal of Rail Transport Planning and Management Volume 21 p.1-15.

Arthur Guijt, Ngoc Hoang Luong, Peter A.N. Bosman, Mathijs de Weerdt (2022), On the impact of linkage learning, gene-pool optimal mixing, and non-redundant encoding on permutation optimization, In Swarm and Evolutionary Computation Volume 70.

G. Veviurko, J.W. Böhmer, Laurens Mackay, M.M. de Weerdt (2022), Surrogate DC Microgrid Models for Optimization of Charging Electric Vehicles under Partial Observability, In Energies Volume 15.

Ksenija Stepanovic, Jichen Wu, Rob Everhardt, Mathijs de Weerdt (2022), Unlocking the Flexibility of District Heating Pipeline Energy Storage with Reinforcement Learning, In Energies Volume 15 p.1-25.