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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).

Artikel | Veröffentlicht | Englisch
Autor*in
Grumbach, FelixFH Bielefeld ; Müller, Anna; Reusch, Pascal; Trojahn, Sebastian
Abstract
This feasibility study utilized regression models to predict makespan robustness in dynamic production processes with uncertain processing times. Previous methods for robustness determination were computationally intensive (Monte Carlo experiments) or inaccurate (surrogate measures). However, calculating robustness efficiently is crucial for field-synchronous scheduling techniques. Regression models with multiple input features considering uncertain processing times on the critical path outperform traditional surrogate measures. Well-trained regression models internalize the behavior of a dynamic simulation and can quickly predict accurate robustness (correlation: r>0.98). The proposed method was successfully applied to a permutation flow shop scheduling problem, balancing makespan and robustness. Integrating regression models into a metaheuristic model, schedules could be generated that have a similar quality to using Monte Carlo experiments. These results suggest that employing machine learning techniques for robustness prediction could be a promising and efficient alternative to traditional approaches. This work is an addition to our previous extensive study about creating robust stable schedules based on deep reinforcement learning and is part of the applied research project, Predictive Scheduling.
Erscheinungsjahr
Zeitschriftentitel
Processes
Band
11
Zeitschriftennummer
4
Artikelnummer
1267
eISSN
FH-PUB-ID

Zitieren

Grumbach, Felix ; Müller, Anna ; Reusch, Pascal ; Trojahn, Sebastian: Robustness Prediction in Dynamic Production Processes—A New Surrogate Measure Based on Regression Machine Learning. In: Processes Bd. 11, MDPI AG (2023), Nr. 4
Grumbach F, Müller A, Reusch P, Trojahn S. Robustness Prediction in Dynamic Production Processes—A New Surrogate Measure Based on Regression Machine Learning. Processes. 2023;11(4). doi:10.3390/pr11041267
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
@article{Grumbach_Müller_Reusch_Trojahn_2023, title={Robustness Prediction in Dynamic Production Processes—A New Surrogate Measure Based on Regression Machine Learning}, volume={11}, DOI={10.3390/pr11041267}, number={41267}, journal={Processes}, publisher={MDPI AG}, author={Grumbach, Felix and Müller, Anna and Reusch, Pascal and Trojahn, Sebastian}, year={2023} }
Grumbach, Felix, Anna Müller, Pascal Reusch, and Sebastian Trojahn. “Robustness Prediction in Dynamic Production Processes—A New Surrogate Measure Based on Regression Machine Learning.” Processes 11, no. 4 (2023). https://doi.org/10.3390/pr11041267.
F. Grumbach, A. Müller, P. Reusch, and S. Trojahn, “Robustness Prediction in Dynamic Production Processes—A New Surrogate Measure Based on Regression Machine Learning,” Processes, vol. 11, no. 4, 2023.
Grumbach, Felix, et al. “Robustness Prediction in Dynamic Production Processes—A New Surrogate Measure Based on Regression Machine Learning.” Processes, vol. 11, no. 4, 1267, MDPI AG, 2023, doi:10.3390/pr11041267.
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