Optimization of global production scheduling with deep reinforcement learning

作者：Bernd Waschneck, André Reichstaller, Lenz Belzner, Thomas Altenmüller, Thomas Bauernhansl, Alexander Knapp, Andreas Kyek · 发表于：Procedia CIRP · 年份：2018 · DOI：10.1016/j.procir.2018.03.212 · 被引用次数：364 · 研究领域：Scheduling and Optimization Algorithms、Advanced Manufacturing and Logistics Optimization、Reinforcement Learning in Robotics

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.