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Optimization of global production scheduling with deep reinforcement learning

: Waschneck, Bernd,; Reichstaller, André; Belzner, Lenz; Altenmüller, Thomas; Bauernhansl, Thomas; Knapp, Alexander; Kyek, Andreas

Fulltext ()

Procedia CIRP 72 (2018), pp.1264-1269
ISSN: 2212-8271
Conference on Manufacturing Systems (CMS) <51, 2018, Stockholm>
Journal Article, Conference Paper, Electronic Publication
Fraunhofer IPA ()
Bestärkendes Lernen; Fertigungsplanung; maschinelles Lernen

Industrie 4.0 introduces decentralized, self-organizing and self-learning systems for production control. At the same time, new machine learning algorithms are getting increasingly powerful and solve real world problems. We apply Google DeepMind's Deep Q Network (DQN) agent algorithm for Reinforcement Learning (RL) to production scheduling to achieve the Industrie 4.0 vision for production control. In an RL environment cooperative DQN agents, which utilize deep neural networks, are trained with user-defined objectives to optimize scheduling. We validate our system with a small factory simulation, which is modeling an abstracted frontend-of-line semiconductor production facility.