Generic Multi-Agent Reinforcement Learning Approach for Flexible Job-Shop Scheduling
1st ed. 2022
by
Schirin Bar
Book Details
Format
Paperback / Softback
ISBN-10
3658391782
ISBN-13
9783658391782
Edition
1st ed. 2022
Publisher
Springer Fachmedien Wiesbaden
Imprint
Springer Vieweg
Country of Manufacture
GB
Country of Publication
GB
Publication Date
Oct 2nd, 2022
Print length
148 Pages
Product Classification:
Business mathematics & systemsProduction engineeringArtificial intelligenceMachine learning
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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.
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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