TY - JOUR A1 - Waschneck, Bernd A1 - Reichstaller, André A1 - Belzner, Lenz A1 - Altenmüller, Thomas A1 - Bauernhansl, Thomas A1 - Knapp, Alexander A1 - Kyek, Andreas T1 - Optimization of global production scheduling with deep reinforcement learning JF - Procedia CIRP N2 - 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. UR - https://doi.org/10.1016/j.procir.2018.03.212 KW - Production Scheduling KW - Reinforcement Learning KW - Machine Learning in Manufacturing Y1 - 2018 UR - https://doi.org/10.1016/j.procir.2018.03.212 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-15735 SN - 2212-8271 VL - 2018 IS - 72 SP - 1264 EP - 1269 PB - Elsevier CY - Amsterdam ER - TY - CHAP A1 - Waschneck, Bernd A1 - Reichstaller, André A1 - Belzner, Lenz A1 - Altenmüller, Thomas A1 - Bauernhansl, Thomas A1 - Knapp, Alexander A1 - Kyek, Andreas T1 - Deep reinforcement learning for semiconductor production scheduling T2 - 2018 29th Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC) UR - https://doi.org/10.1109/ASMC.2018.8373191 KW - Production Scheduling KW - Reinforcement Learning KW - Machine Learning KW - Semiconductor Manufacturing Y1 - 2018 UR - https://doi.org/10.1109/ASMC.2018.8373191 SN - 978-1-5386-3748-7 SN - 2376-6697 SP - 301 EP - 306 PB - IEEE CY - Piscataway ER -