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Reinforcement Learning Based Production Control of Semi-automated Manufacturing Systems

Overbeck, Leonard; Hugues, Adrien; May, Marvin Carl; Kuhnle, Andreas; Lanza, Gisela

Abstract:
In an environment which is marked by an increasing speed of changes, industrial companies have to be able to quickly adapt to new market demands and innovative technologies. This leads to a need for continuous adaption of existing production systems and the optimization of their production control. To tackle this problem digitalization of production systems has become essential for new and existing systems. Digital twins based on simulations of real production systems allow the simplification of analysis processes and, thus, a better understanding of the systems, which leads to broad optimization possibilities. In parallel, machine learning methods can be integrated to process the numerical data and discover new production control strategies. In this work, these two methods are combined to derive a production control logic in a semi-automated production system based on the chaku-chaku principle. A reinforcement learning method is integrated into the digital twin to autonomously learn a superior production control logic for the distribution of tasks between the different workers on a production line.

By analyzing the influence of different reward shaping and hyper-parameter optimization on the quality and stability of the results obtained, the use of a well-configured policy-based algorithm enables an efficient management of the workers and the deduction of an optimal production control logic for the production system. ... mehr


Verlagsausgabe §
DOI: 10.5445/IR/1000140336
Veröffentlicht am 25.11.2021
Originalveröffentlichung
DOI: 10.1016/j.procir.2021.10.027
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Produktionstechnik (WBK)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2021
Sprache Englisch
Identifikator ISSN: 2212-8271
KITopen-ID: 1000140336
Erschienen in 9th CIRP Global Web Conference – Sustainable, resilient, and agile manufacturing and service operations : Lessons from COVID-19. Ed.: K. Medini
Veranstaltung 9th CIRP Global Web Conference (CIRPe 2021), Online, 26.10.2021 – 28.10.2021
Verlag Elsevier
Seiten 170-175
Serie Procedia CIRP ; 103
Schlagwörter Machine Learning; Reinforcement Learning; Digital Twin; Production Control; Task Allocation; Productivity
Nachgewiesen in Scopus
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