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Reinforcement learning for an intelligent and autonomous production control of complex job-shops under time constraints

Altenmüller, Thomas; Stüker, Tillmann; Waschneck, Bernd; Kuhnle, Andreas; Lanza, Gisela

Reinforcement learning (RL) offers promising opportunities to handle the ever-increasing complexity in managing modern production systems. We apply a Q-learning algorithm in combination with a process-based discrete-event simulation in order to train a self-learning, intelligent, and autonomous agent for the decision problem of order dispatching in a complex job shop with strict time constraints. For the first time, we combine RL in production control with strict time constraints. The simulation represents the characteristics of complex job shops typically found in semiconductor manufacturing. A real-world use case from a wafer fab is addressed with a developed and implemented framework. The performance of an RL approach and benchmark heuristics are compared. It is shown that RL can be successfully applied to manage order dispatching in a complex environment including time constraints. An RL-agent with a gain function rewarding the selection of the least critical order with respect to time-constraints beats heuristic rules strictly by picking the most critical lot first. Hence, this work demonstrates that a self-learning agent can successfully manage time constraints with the agent performing better than the traditional benchmark, a time-constraint heuristic combining due date deviations and a classical first-in-first-out approach.

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Verlagsausgabe §
DOI: 10.5445/IR/1000123326
Veröffentlicht am 04.09.2020
DOI: 10.1007/s11740-020-00967-8
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Produktionstechnik (WBK)
Publikationstyp Zeitschriftenaufsatz
Publikationsmonat/-jahr 06.2020
Sprache Englisch
Identifikator ISSN: 0944-6524, 1863-7353
KITopen-ID: 1000123326
Erschienen in Production engineering
Band 14
Heft 3
Seiten 319–328
Vorab online veröffentlicht am 07.06.2020
Nachgewiesen in Scopus
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