Feldsynchrone Ablaufplanung dynamischer Fertigungsprozesse mit Techniken des maschinellen Lernens [kumulative Dissertation]
F. Grumbach, Feldsynchrone Ablaufplanung dynamischer Fertigungsprozesse mit Techniken des maschinellen Lernens [kumulative Dissertation], Universitäts- und Landesbibliothek Sachsen-Anhalt, Bernburg, 2024.

Smaller Batches, Bigger Gains? Investigating the Impact of Batch Sizes on Reinforcement Learning Based Real-World Production Scheduling
A. Müller, F. Grumbach, M. Sabatelli, in: IEEE (Ed.), 2024 IEEE 29th International Conference on Emerging Technologies and Factory Automation (ETFA), IEEE, 2024, pp. 01–08.

Demystifying Reinforcement Learning in Production Scheduling via Explainable AI
D. Fischer, H.M. Hüsener, F. Grumbach, L. Vollenkemper, A. Müller, P. Reusch, ArXiv (2024).

Optimizing Sales Forecasts through Automated Integration of Market Indicators
L. Döring, F. Grumbach, P. Reusch, ArXiv (2024).

Robust Human-Centered Assembly Line Scheduling with Reinforcement Learning
F. Grumbach, A. Müller, L. Vollenkemper, in: M. Freitag, A. Kinra, H. Kotzab, N. Megow (Eds.), Dynamics in Logistics. Proceedings of the 9th International Conference LDIC 2024, Bremen, Germany, Springer Nature Switzerland, Cham, 2024, pp. 223–234.

Reinforcement Learning for Two-Stage Permutation Flow Shop Scheduling—A Real-World Application in Household Appliance Production
A. Müller, F. Grumbach, F. Kattenstroth, IEEE Access 12 (2024) 11388–11399.

Humanzentrierte Ablaufplanung von Montagelinien/Human-centered scheduling in assembly lines - Plug and play: Efficient algorithms minimize stress in flow shops
L. Vollenkemper, F. Grumbach, M. Kohlhase, P. Reusch, Wt Werkstattstechnik Online 113 (2023) 158–163.

Predicting processing times in high mix low volume job shops
A. Müller, F. Grumbach, in: E. Glistau, S. Trojahn (Eds.), 16th International Doctoral Students Workshop on Logistics, Supply Chain and Production Management, Otto von Guericke University Library, Magdeburg, Germany, 2023.

A Memetic Algorithm With Reinforcement Learning for Sociotechnical Production Scheduling
F. Grumbach, N.E.A. Badr, P. Reusch, S. Trojahn, IEEE Access 11 (2023) 68760–68775.

Robustness Prediction in Dynamic Production Processes—A New Surrogate Measure Based on Regression Machine Learning
F. Grumbach, A. Müller, P. Reusch, S. Trojahn, Processes 11 (2023).

Robust-stable scheduling in dynamic flow shops based on deep reinforcement learning
F. Grumbach, A. Müller, P. Reusch, S. Trojahn, Journal of Intelligent Manufacturing (2022).