Khayyati, S. (2025). Machine Learning Based Analysis of Queueing Systems [Paper presentation]. Research Day - HEC Liege. |
Tan, B., Karabağ, O., & Khayyati, S. (2024). Energy-efficient production control of a make-to-stock system with buffer- and time-based policies. International Journal of Production Research, 1-19. doi:10.1080/00207543.2023.2298488 |
Tan, B., Karabağ, O., & Khayyati, S. (August 2023). Production and energy mode control of a production-inventory system. European Journal of Operational Research, 308 (3), 1176 - 1187. doi:10.1016/j.ejor.2022.12.021 |
Pirayesh Neghab, D., Khayyati, S., & Karaesmen, F. (16 October 2022). An integrated data-driven method using deep learning for a newsvendor problem with unobservable features. European Journal of Operational Research, 302 (2), 482 - 496. doi:10.1016/j.ejor.2021.12.047 |
Khayyati, S., & Tan, B. (2022). Supervised-learning-based approximation method for multi-server queueing networks under different service disciplines with correlated interarrival and service times. International Journal of Production Research, 60 (17), 5176 - 5200. doi:10.1080/00207543.2021.1951448 |
Khayyati, S., & Tan, B. (2022). A machine learning approach for implementing data-driven production control policies. International Journal of Production Research, 60 (10), 3107 - 3128. doi:10.1080/00207543.2021.1910872 |
Tan, B., & Khayyati, S. (2022). Supervised learning-based approximation method for single-server open queueing networks with correlated interarrival and service times. International Journal of Production Research, 60 (22), 6822 - 6847. doi:10.1080/00207543.2021.1887536 |
Khayyati, S., & Tan, B. (July 2021). A lab-scale manufacturing system environment to investigate data-driven production control approaches. Journal of Manufacturing Systems, 60, 283 - 297. doi:10.1016/j.jmsy.2021.06.002 |
Khayyati, S., & Tan, B. (August 2020). Data-driven control of a production system by using marking-dependent threshold policy. International Journal of Production Economics, 226, 107607. doi:10.1016/j.ijpe.2019.107607 |