Optimization of global production scheduling with deep reinforcement learning

By Bernd Waschneck, André Reichstaller, Lenz Belzner, Thomas Altenmüller, T. Bauernhansl, Alexander Knapp, A. Kyek. 2018

344 citations recorded by the source corpus.

Abstract 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.

Subfields: Artificial Intelligence, Industrial and Manufacturing Engineering

View Paper: https://www.semanticscholar.org/paper/f3559b9fc51f65bbf4b8db57591ea1f2a51b5fbb

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In Artificial Intelligence

Dmitry A. Ivanov

In Industrial and Manufacturing Engineering

Citations in the corpus

A supervised machine learning approach to data-driven simulation of resilient supplier selection in digital manufacturing (2019, background): “[91], Waschneck et al. [92] and Li et al. [93] use RL to solve the JSSP.”
Artificial Intelligence in Advanced Manufacturing: Current Status and Future Outlook (2020, unknown): “[51 – 54], has been reported for distinct applicable scenarios.”
A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics (2021, methodology): “Learning DQN Optimization of the workflow in a semiconductor wafer processing plant [97]”