Pieter Leyman
![Pieter Leyman](/assets/img/people/PieterLeyman.jpg)
Pieter Leyman
tel.: +32 9 264 55 02
research unit: Industrial Systems Engineering (ISyE)
Pieter Leyman is an assistant professor in Sustainable Production Processes at the Industrial System Engineering research group at the Faculty of Engineering and Architecture of Ghent University. He obtained his PhD in Applied Economic Sciences from the same university in 2016, after which he worked as a postdoctoral researcher at both KU Leuven and at University of Antwerp, on top of a research stay at Leiden University in the Netherlands. He is furthermore an affiliate member of Flanders Make, the strategic research center for the Flemish manufacturing industry. His main research interest are the development of metaheuristic algorithms for NP-hard combinatorial optimization problems, next to their integration with machine learning techniques to further improve performance. He applies these techniques in an energy-aware production scheduling context, in order to contribute to a more sustainable society.
Keywords: Optimization, metaheuristics, explainable AI, scheduling, instance hardness
- Brughmans, D., Leyman, P. and Martens, D. (2024). NICE: An algorithm for nearest instance counterfactual explanations. Data Mining and Knowledge Discovery.
- Jooken, J., Leyman, P., Wauters, T. and De Causmaecker, P. (2023). Exploring search space trees using an adapted version of Monte Carlo tree search for a combinatorial optimization problem. Computers & Operations Research, 150: 106070.
- Jooken, J., Leyman, P. and De Causmaecker, P. (2022). A new class of hard problem instances for the 0-1 knapsack problem. European Journal of Operational Research, 301(3), 841-854.
- Tran, D.H., Leyman, P. and De Causmaecker, P. (2022). Adaptive recommendation system for taxi drivers with load balancing problem. Computers & Industrial Engineering, 169: 108187.
- Leyman, P. and Vanhoucke, M. (2015). A new scheduling technique for the resource-constrained project scheduling problem with discounted cash flows. International Journal of Production Research, 53(9): 2771-2786.