Published in Master Thesis, 2017
Optimizing mixed-integer linear programs with decision diagrams by decomposing the problem using benders decomposition.
Recommended citation: Van der Linden, K. (2017). Decision Diagrams for Decomposed Mixed Integer Linear Programs [Master thesis, Delft University of Technology]. TU Delft Research Repository.
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Published in Proceedings of the 15th International Conference on the European Energy Market (EEM), 2018
We contribute a stochastic optimization method for EV aggregators that models uncertainty in imbalance prices, reserve prices, and reserve acceptance probabilities, enabling optimal charging and discharging strategies that outperform deterministic and quantity-only bid approaches by lowering costs.
Recommended citation: Van der Linden, K., de Weerdt, M., & Morales-España, G. (2018). "Optimal Non-Zero Price Bids for EVs in Energy and Reserves Markets using Stochastic Optimization." Proceedings of the 15th International Conference on the European Energy Market (EEM).
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Published in Proceedings of IJCAI-18, 2018
For about 20 variants of charging scheduling problems, we show that the problem is either in P or weakly NP-hard and we show that 10 variants are strongly NP-hard.
Recommended citation: De Weerdt, M., Albert, M., Conitzer, V., & van der Linden, K. (2018). "Complexity of Scheduling Charging in the Smart Grid." Proceedings of IJCAI-18, 4736-4742.
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Published in ArXiv Preprint, 2020
We analyze the potential of extending the multi-agent path finding model to provide bounds for the train unit shunting and service problem, which helps to evaluate heuristic solutions and inform investment decisions on yard capacity.
Recommended citation: Mulderij, J., Huisman, B., Tönissen, D., van der Linden, K., & de Weerdt, M. (2020). "Train Unit Shunting and Servicing: A Real-Life Application of Multi-Agent Path Finding." arXiv preprint, arXiv:2006.10422.
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Published in Electric Power Systems Research, 2021
We compare state-of-the-art methods for bidding strategies in the day-ahead and reserve markets for large fleets of flexible loads like EVs.
Recommended citation: Romero, N., Van der Linden, K., Morales-España, G., & de Weerdt, M., (2021). "Stochastic bidding of volume and price in constrained energy and reserve markets." Electric Power Systems Research 191, 106868.
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Published in Energies, 2021
Recommended citation: Van der Linden, K., Romero, N., & de Weerdt, M. M. (2021). "Benchmarking Flexible Electric Loads Scheduling Algorithms." Energies 14(5), 1269.
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Published in Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2021
We developed TORS, an event-based simulator for the Train Unit Shunting and Servicing problem, which enables research on realistic applications of multi-agent pathfinding by providing users with a state, feasible actions, and real-time outcome calculations.
Recommended citation: Van der Linden, J. G. M., Mulderij, J., Huisman, B., den Ouden, J. W., van den Akker, M., Hoogeveen, H., & de Weerdt, M. M. (2021). "TORS: A Train Unit Shunting and Servicing Simulator." Proceedings of AAMAS-21, 1773-1775.
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Published in Advances in NeurIPS-22, 2022
We use dynamic programming to find optimal fair decision trees orders of magnitude faster than previous methods.
Recommended citation: Van der Linden, J. G. M., de Weerdt, M. M., & Demirović, E. (2022). "Fair and Optimal Decision Trees: A Dynamic Programming Approach." Advances in NeurIPS-22, 38899-38911.
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Published in Advances in NeurIPS-23, 2023
We prove necessary and sufficient conditions for the use of DP for optimal decision trees and provide a framework STreeD that can optimize trees for a variety of objectives and constraints.
Recommended citation: Van der Linden, J. G. M., de Weerdt, M. M., & Demirović, E. (2023). "Necessary and Sufficient Conditions for Optimal Decision Trees Using Dynamic Programming." Advances in NeurIPS-23, 9173-9212.
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Published in Proceedings of AAAI-24, 2024
We provide the first approach to optimal survival trees (under the proportional hazards assumption).
Recommended citation: Huisman, T., van der Linden, J. G. M., & Demirović, E. (2024). "Optimal Survival Trees: A Dynamic Programming Approach." Proceedings of AAAI-24, 12680-12688.
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Published in Proceedings of ICML-24, 2024
We use DP to generate optimal regression trees with constant and (simple) linear regression models in the leaf node. Our method improves scalability by one or more orders of magnitude in comparison to the state-of-the-art.
Recommended citation: Van den Bos, M., van der Linden, J. G. M., & Demirović, E. (2024). "Piecewise Constant and Linear Regression Trees: An Optimal Dynamic Programming Approach." Proceedings of ICML-24.
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Published:
B-FELSA is a framework for benchmarking flexible electric load scheduling algorithms.
Recommended citation: Van der Linden, K., Romero, N., & de Weerdt, M. M. (2021). "Benchmarking Flexible Electric Loads Scheduling Algorithms." Energies 14(5), 1269.
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Published:
TORS is an event-based simulator for the Train Unit Shunting and Servicing problem, which enables research on realistic applications of multi-agent pathfinding by providing users with a state, feasible actions, and real-time outcome calculations.
Recommended citation: Van der Linden, J. G. M., Mulderij, J., Huisman, B., den Ouden, J. W., van den Akker, M., Hoogeveen, H., & de Weerdt, M. M. (2021). "TORS: A Train Unit Shunting and Servicing Simulator." Proceedings of AAMAS-21, 1773-1775.
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Published:
DPF constructs fair and optimal binary classification trees. It minimizes misclassification score, while respecting a demographic parity fairness constraint, for a given size (depth, number of nodes). It can also generate the full Pareto front while optimizing both fairness and accuracy.
Recommended citation: Van der Linden, J. G. M., de Weerdt, M. M., & Demirović, E. (2022). "Fair and Optimal Decision Trees: A Dynamic Programming Approach." Advances in NeurIPS-22, 38899-38911.
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Published:
STreeD is a DP framework that can optimize trees for a variety of objectives and constraints.
Recommended citation: Van der Linden, J. G. M., de Weerdt, M. M., & Demirović, E. (2023). "Necessary and Sufficient Conditions for Optimal Decision Trees Using Dynamic Programming." Advances in NeurIPS-23, 9173-9212.
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Undergraduate course, University 1, Department, 2014
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Workshop, University 1, Department, 2015
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