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Optimization of global production scheduling with deep reinforcement learning

  • 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.

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Metadaten
Author:Bernd Waschneck, André Reichstaller, Lenz BelznerORCiD, Thomas Altenmüller, Thomas Bauernhansl, Alexander Knapp, Andreas Kyek
Language:English
Document Type:Article
Conference:51st CIRP Conference on Manufacturing Systems, Stockholm (Sweden), 16.-18.05.2018
Year of first Publication:2018
published in (English):Procedia CIRP
Publisher:Elsevier
Place of publication:Amsterdam
ISSN:2212-8271
Volume:2018
Issue:72
First Page:1264
Last Page:1269
Review:peer-review
Open Access:ja
Version:published
Tag:Machine Learning in Manufacturing; Production Scheduling; Reinforcement Learning
URN:urn:nbn:de:bvb:573-15735
Related Identifier:https://doi.org/10.1016/j.procir.2018.03.212
Release Date:2022/04/13