PUBLIKATIONSSERVER

Felix Grumbach

https://orcid.org/0000-0001-6348-7897

11 Publikationen

Alle markieren

[11]
2024 | Dissertation | FH-PUB-ID: 4392 | OA
Grumbach, F. (2024). Feldsynchrone Ablaufplanung dynamischer Fertigungsprozesse mit Techniken des maschinellen Lernens [kumulative Dissertation]. Bernburg: Universitäts- und Landesbibliothek Sachsen-Anhalt. https://doi.org/10.25673/115290
HSBI-PUB | Dateien verfügbar | DOI | Download (ext.)
 
[10]
2024 | Konferenzbeitrag | FH-PUB-ID: 5467
Müller, A., Grumbach, F., & Sabatelli, M. (2024). Smaller Batches, Bigger Gains? Investigating the Impact of Batch Sizes on Reinforcement Learning Based Real-World Production Scheduling. In IEEE (Ed.), 2024 IEEE 29th International Conference on Emerging Technologies and Factory Automation (ETFA) (pp. 01–08). Padova, Italy : IEEE. https://doi.org/10.1109/ETFA61755.2024.10711145
HSBI-PUB | DOI
 
[9]
2024 | Artikel | FH-PUB-ID: 5470 | OA
Fischer, D., Hüsener, H. M., Grumbach, F., Vollenkemper, L., Müller, A., & Reusch, P. (2024). Demystifying Reinforcement Learning in Production Scheduling via Explainable AI. ArXiv. https://doi.org/10.48550/ARXIV.2408.09841
HSBI-PUB | DOI | Download (ext.)
 
[8]
2024 | Artikel | FH-PUB-ID: 5472 | OA
Döring, L., Grumbach, F., & Reusch, P. (2024). Optimizing Sales Forecasts through Automated Integration of Market Indicators. ArXiv. https://doi.org/10.48550/ARXIV.2406.07564
HSBI-PUB | DOI | Download (ext.)
 
[7]
2024 | Konferenzbeitrag | FH-PUB-ID: 4567
Grumbach, F., Müller, A., & Vollenkemper, L. (2024). Robust Human-Centered Assembly Line Scheduling with Reinforcement Learning. In M. Freitag, A. Kinra, H. Kotzab, & N. Megow (Eds.), Dynamics in Logistics. Proceedings of the 9th International Conference LDIC 2024, Bremen, Germany (pp. 223–234). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-56826-8_17
HSBI-PUB | DOI
 
[6]
2024 | Artikel | FH-PUB-ID: 4283 | OA
Müller, A., Grumbach, F., & Kattenstroth, F. (2024). Reinforcement Learning for Two-Stage Permutation Flow Shop Scheduling—A Real-World Application in Household Appliance Production. IEEE Access, 12, 11388–11399. https://doi.org/10.1109/ACCESS.2024.3355269
HSBI-PUB | Dateien verfügbar | DOI | Download (ext.)
 
[5]
2023 | Konferenzbeitrag | FH-PUB-ID: 4394 | OA
Müller, A., & Grumbach, F. (2023). Predicting processing times in high mix low volume job shops. In E. Glistau & S. Trojahn (Eds.), 16th International Doctoral Students Workshop on Logistics, Supply Chain and Production Management. Magdeburg: Otto von Guericke University Library, Magdeburg, Germany. https://doi.org/10.25673/103491
HSBI-PUB | DOI | Download (ext.)
 
[4]
2023 | Artikel | FH-PUB-ID: 3766 | OA
Grumbach, F., Badr, N. E. A., Reusch, P., & Trojahn, S. (2023). A Memetic Algorithm With Reinforcement Learning for Sociotechnical Production Scheduling. IEEE Access, 11, 68760–68775. https://doi.org/10.1109/ACCESS.2023.3292548
HSBI-PUB | Dateien verfügbar | DOI | Download (ext.)
 
[3]
2023 | Artikel | FH-PUB-ID: 2849 | OA
Vollenkemper, L., Grumbach, F., Kohlhase, M., & Reusch, P. (2023). Humanzentrierte Ablaufplanung von Montagelinien/Human-centered scheduling in assembly lines - Plug and play: Efficient algorithms minimize stress in flow shops. Wt Werkstattstechnik Online, 113(04), 158–163. https://doi.org/10.37544/1436-4980-2023-04-58
HSBI-PUB | DOI | Download (ext.)
 
[2]
2023 | Artikel | FH-PUB-ID: 2848 | OA
Grumbach, F., Müller, A., Reusch, P., & Trojahn, S. (2023). Robustness Prediction in Dynamic Production Processes—A New Surrogate Measure Based on Regression Machine Learning. Processes, 11(4). https://doi.org/10.3390/pr11041267
HSBI-PUB | DOI | Download (ext.)
 
[1]
2022 | Artikel | FH-PUB-ID: 2295 | OA
Grumbach, F., Müller, A., Reusch, P., & Trojahn, S. (2022). Robust-stable scheduling in dynamic flow shops based on deep reinforcement learning. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-022-02069-x
HSBI-PUB | DOI | Download (ext.)
 
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11 Publikationen

Alle markieren

[11]
2024 | Dissertation | FH-PUB-ID: 4392 | OA
Grumbach, F. (2024). Feldsynchrone Ablaufplanung dynamischer Fertigungsprozesse mit Techniken des maschinellen Lernens [kumulative Dissertation]. Bernburg: Universitäts- und Landesbibliothek Sachsen-Anhalt. https://doi.org/10.25673/115290
HSBI-PUB | Dateien verfügbar | DOI | Download (ext.)
 
[10]
2024 | Konferenzbeitrag | FH-PUB-ID: 5467
Müller, A., Grumbach, F., & Sabatelli, M. (2024). Smaller Batches, Bigger Gains? Investigating the Impact of Batch Sizes on Reinforcement Learning Based Real-World Production Scheduling. In IEEE (Ed.), 2024 IEEE 29th International Conference on Emerging Technologies and Factory Automation (ETFA) (pp. 01–08). Padova, Italy : IEEE. https://doi.org/10.1109/ETFA61755.2024.10711145
HSBI-PUB | DOI
 
[9]
2024 | Artikel | FH-PUB-ID: 5470 | OA
Fischer, D., Hüsener, H. M., Grumbach, F., Vollenkemper, L., Müller, A., & Reusch, P. (2024). Demystifying Reinforcement Learning in Production Scheduling via Explainable AI. ArXiv. https://doi.org/10.48550/ARXIV.2408.09841
HSBI-PUB | DOI | Download (ext.)
 
[8]
2024 | Artikel | FH-PUB-ID: 5472 | OA
Döring, L., Grumbach, F., & Reusch, P. (2024). Optimizing Sales Forecasts through Automated Integration of Market Indicators. ArXiv. https://doi.org/10.48550/ARXIV.2406.07564
HSBI-PUB | DOI | Download (ext.)
 
[7]
2024 | Konferenzbeitrag | FH-PUB-ID: 4567
Grumbach, F., Müller, A., & Vollenkemper, L. (2024). Robust Human-Centered Assembly Line Scheduling with Reinforcement Learning. In M. Freitag, A. Kinra, H. Kotzab, & N. Megow (Eds.), Dynamics in Logistics. Proceedings of the 9th International Conference LDIC 2024, Bremen, Germany (pp. 223–234). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-56826-8_17
HSBI-PUB | DOI
 
[6]
2024 | Artikel | FH-PUB-ID: 4283 | OA
Müller, A., Grumbach, F., & Kattenstroth, F. (2024). Reinforcement Learning for Two-Stage Permutation Flow Shop Scheduling—A Real-World Application in Household Appliance Production. IEEE Access, 12, 11388–11399. https://doi.org/10.1109/ACCESS.2024.3355269
HSBI-PUB | Dateien verfügbar | DOI | Download (ext.)
 
[5]
2023 | Konferenzbeitrag | FH-PUB-ID: 4394 | OA
Müller, A., & Grumbach, F. (2023). Predicting processing times in high mix low volume job shops. In E. Glistau & S. Trojahn (Eds.), 16th International Doctoral Students Workshop on Logistics, Supply Chain and Production Management. Magdeburg: Otto von Guericke University Library, Magdeburg, Germany. https://doi.org/10.25673/103491
HSBI-PUB | DOI | Download (ext.)
 
[4]
2023 | Artikel | FH-PUB-ID: 3766 | OA
Grumbach, F., Badr, N. E. A., Reusch, P., & Trojahn, S. (2023). A Memetic Algorithm With Reinforcement Learning for Sociotechnical Production Scheduling. IEEE Access, 11, 68760–68775. https://doi.org/10.1109/ACCESS.2023.3292548
HSBI-PUB | Dateien verfügbar | DOI | Download (ext.)
 
[3]
2023 | Artikel | FH-PUB-ID: 2849 | OA
Vollenkemper, L., Grumbach, F., Kohlhase, M., & Reusch, P. (2023). Humanzentrierte Ablaufplanung von Montagelinien/Human-centered scheduling in assembly lines - Plug and play: Efficient algorithms minimize stress in flow shops. Wt Werkstattstechnik Online, 113(04), 158–163. https://doi.org/10.37544/1436-4980-2023-04-58
HSBI-PUB | DOI | Download (ext.)
 
[2]
2023 | Artikel | FH-PUB-ID: 2848 | OA
Grumbach, F., Müller, A., Reusch, P., & Trojahn, S. (2023). Robustness Prediction in Dynamic Production Processes—A New Surrogate Measure Based on Regression Machine Learning. Processes, 11(4). https://doi.org/10.3390/pr11041267
HSBI-PUB | DOI | Download (ext.)
 
[1]
2022 | Artikel | FH-PUB-ID: 2295 | OA
Grumbach, F., Müller, A., Reusch, P., & Trojahn, S. (2022). Robust-stable scheduling in dynamic flow shops based on deep reinforcement learning. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-022-02069-x
HSBI-PUB | DOI | Download (ext.)
 
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Zitationsstil: APA

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