Igor Smit obtained his dual MSc. degree in Data Science in Engineering and Operations Management & Logistics cum laude at the Departments of Mathematics & Computer Science and Industrial Engineering & Innovation Sciences at Eindhoven University of Technology (Netherlands). His MSc. dissertation “Learning to Be Efficient and Fair for Collaborative Order Picking” was carried out on the topic of deep reinforcement learning for online combinatorial optimization under the supervision of dr. Zaharah Bukhsh, dr. Yingqian Zhang, and prof. dr. Mykola Pechenizkiy. Currently, he is a Ph.D. student at Eindhoven University of Technology – Data Science Domain – Process Analytics cluster. Smit works on the Learning and Explaining Optimization project under the supervision of prof. dr. ir. Wim Nuijten.
| Position: | PhD |
| Room: | Neuron 1.118 |
| Tel (internal): | |
| Links: | |
| External links: | TU/e page |
Recent courses
Recent presentations
Recent projects
Recent publications
- Neural Combinatorial Optimization for Stochastic Flexible Job Shop Scheduling Problems - Smit, I. G., Wu, Y., Troubil, P., Zhang, Y., & Nuijten, W. P. M. (2025). Neural Combinatorial Optimization for Stochastic Flexible Job Shop Scheduling Problems. In T. Walsh, J. Shah, & Z. Kolter (Eds.), Proceedings of the 39th Annual AAAI Conference on Artificial Intelligence: AAAI-25 Technical Tracks 25 (pp. 26678-26687). (Proceedings of the AAAI Conference Read More ...
- Graph Neural Networks for Job Shop Scheduling Problems: A Survey - Smit, I. G., Zhou, J., Reijnen, R., Wu, Y., Chen, J., Zhang, C., Bukhsh, Z., Zhang, Y. & Nuijten, W. P. M. (2025). Graph Neural Networks for Job Shop Scheduling Problems: A Survey. Computers and Operations Research, 176, Article 106914. https://doi.org/10.1016/j.cor.2024.106914 Abstract Job shop scheduling problems (JSSPs) represent a critical and challenging class of combinatorial Read More ...