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About this book
The production control of flexible manufacturing systems is a relevant component that must go along with the requirements of being flexible in terms of new product variants, new machine skills and reaction to unforeseen events during runtime. This work focuses on developing a reactive job-shop scheduling system for flexible and re-configurable manufacturing systems. Reinforcement Learning approaches are therefore investigated for the concept of multiple agents that control products including transportation and resource allocation.
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Table of contents (9 chapters)
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Front Matter
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Back Matter
Authors and Affiliations
About the author
About the author
Schirin Bär researched at the RWTH-Aachen University at the Institute for Information Management in Mechanical Engineering (IMA) on the optimization of production control of flexible manufacturing systems using reinforcement learning. As operations manager and previously as an engineer, she developed and evaluated the research results based on real systems.
Bibliographic Information
Book Title: Generic Multi-Agent Reinforcement Learning Approach for Flexible Job-Shop Scheduling
Authors: Schirin Bär
DOI: https://doi.org/10.1007/978-3-658-39179-9
Publisher: Springer Vieweg Wiesbaden
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2022
Softcover ISBN: 978-3-658-39178-2Published: 02 October 2022
eBook ISBN: 978-3-658-39179-9Published: 01 October 2022
Edition Number: 1
Number of Pages: XXII, 148
Number of Illustrations: 4 b/w illustrations, 35 illustrations in colour
Topics: Machine Learning, Multiagent Systems, Manufacturing, Machines, Tools, Processes, Management of Computing and Information Systems