Funing Li

F. Li et al., “A transformer-based deep reinforcement learning approach for dynamic parallel machine scheduling problem with family setups,” Journal of Intelligent Manufacturing, vol. 35, Art. no. 6, Aug. 2024, [Online]. Available: https://link.springer.com/article/10.1007/s10845-024-02470-8

F. Li, R. Noortwyck, and R. Schulz, “Ein skalierbarer Deep Reinforcement Learning-Ansatz zur Minimierung der Gesamtverspätung bei paralleler Maschinenbelegung,” in Logistics Journal: Proceedings, Wissenschaftliche Beiträge zum 20. WGTL-Kolloquium 2024 in Dresden, in Logistics Journal: Proceedings, vol. 20 (2024). Wissenschaftliche Gesellschaft für Technische Logistik e.V. (WGTL), Oct. 2024. doi: https://doi.org/10.2195/lj_proc_en_li_202410_01.

F. Li, S. Lang, B. Hong, and T. Reggelin, “A two-stage RNN-based deep reinforcement learning approach for solving the parallel machine scheduling problem with due dates and family setups,” Journal of Intelligent Manufacturing, 2023, doi: 10.1007/s10845-023-02094-4.