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Dynamic scheduling in a job-shop production system with reinforcement learning

: Kardos, Csaba; Laflamme, Catherine; Gallina, Viola; Sihn, Wilfried

Fulltext ()

Procedia CIRP 97 (2021), pp.104-109
ISSN: 2212-8271
Conference of Assembly Technology and Systems (CATS) <8, 2020, Online>
Journal Article, Conference Paper, Electronic Publication
Fraunhofer Austria ()
Austria Wien; Bestärkendes Lernen; Simulation; smart factory

Fluctuating customer demands, expected short delivery times and the need for quick order confirmation creates a fast-paced scheduling environment for modern production systems. In this turbulent scene, using the data provided by intelligent elements of cyber-physical production systems opens up new possibilities for dynamic scheduling. The paper introduces a reinforcement learning approach, in particular Q-Learning, to reduce the average lead-time of production orders in a job-shop production system. The intelligent product agents are able to choose a machine for every production step based on real-time information. A performance comparison against standard dispatching rules is given, which shows that in the presented dynamic scheduling use-cases the application of RL reduces the average lead-time.