This image showsFuning Li

Funing Li

M.Sc.

Technical Logistics | Logistic Processes
Insitute of Mechanical Handling and Logistics

Contact

+49 711 685 83698
 +49 711 685 83469

Holzgartenstraße 15 B
70174 Stuttgart
Germany

Subject

  • Optimization of material throughput using deep reinforcement learning (DRL)
  • Material Flow Simulation with AnyLogic

  1. 2025

    1. F. Li, Y. Tian, R. Noortwyck, J. Zhou, L. Kuang, and R. Schulz, “Topology-aware and highly generalizable deep reinforcement learning for efficient retrieval in multi-deep storage systems,” Journal of Intelligent Manufacturing, 2025, doi: doi.org/10.1007/s10845-025-02654-w.

  2. 2024

    1. 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
    2. 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.

  3. 2023

    1. 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.
  • Lecture supervision "Basics of logistics" (attendance and online study programs)
  • Lecture supervision "Automotive logistics" (attendance and online study programs)
  • Supervision of student research projects (term papers bachelor and master theses)
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