Papers I have co-authored.
Fischer, D. et al. (2024). Demystifying Reinforcement Learning in Production Scheduling via Explainable AI. arXiv:2408.09841. https://arxiv.org/abs/2408.09841
Uses a real-world dataset and explainable AI methods (SHAP and Captum) to examine the decisions of a deep reinforcement learning agent. The agent decides which product to produce given constraints on demand, buffer size and storage.
Schwede, C., Fischer, D. (2024). Learning Simulation-Based Digital Twins for Discrete Material Flow Systems: A Review. Winter Simulation Conference 2024, pp. 3070-3081. https://doi.org/10.1109/WSC63780.2024.10838729
A systematic literature review of techniques for learning digital twins in the manufacturing domain.