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Designing an adaptive production control system using reinforcement learning

Kuhnle, A. 1; Kaiser, Jan-Philipp 1; Theiß, F. 1; Stricker, N. 1; Lanza, G. 1
1 Institut für Produktionstechnik (WBK), Karlsruher Institut für Technologie (KIT)

Abstract:

Modern production systems face enormous challenges due to rising customer requirements resulting in complex production systems. The operational efficiency in the competitive industry is ensured by an adequate production control system that manages all operations in order to optimize key performance indicators. Currently, control systems are mostly based on static and model-based heuristics, requiring significant human domain knowledge and, hence, do not match the dynamic environment of manufacturing companies. Data-driven reinforcement learning (RL) showed compelling results in applications such as board and computer games as well as first production applications. This paper addresses the design of RL to create an adaptive production control system by the real-world example of order dispatching in a complex job shop. As RL algorithms are “black box” approaches, they inherently prohibit a comprehensive understanding. Furthermore, the experience with advanced RL algorithms is still limited to single successful applications, which limits the transferability of results. In this paper, we examine the performance of the state, action, and reward function RL design. ... mehr


Verlagsausgabe §
DOI: 10.5445/IR/1000122797
Veröffentlicht am 20.05.2022
Originalveröffentlichung
DOI: 10.1007/s10845-020-01612-y
Scopus
Zitationen: 77
Web of Science
Zitationen: 57
Dimensions
Zitationen: 84
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Produktionstechnik (WBK)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2021
Sprache Englisch
Identifikator ISSN: 0956-5515, 1572-8145
KITopen-ID: 1000122797
Erschienen in Journal of intelligent manufacturing
Verlag Springer
Band 32
Seiten 855–876
Vorab online veröffentlicht am 14.07.2020
Nachgewiesen in Web of Science
Dimensions
Scopus
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