Generic Multi-Agent Reinforcement Learning Approach for Flexible Job-Shop Scheduling (Paperback, 1st ed. 2022)

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.

General

Imprint:	Springer Vieweg
Country of origin:	Germany
Release date:	October 2022
First published:	2022
Authors:	Schirin Bar
Dimensions:	210 x 148mm (L x W)
Format:	Paperback
Pages:	148
Edition:	1st ed. 2022
ISBN-13:	978-3-658-39178-2
Categories:	Books > Computing & IT > Social & legal aspects of computing > Human-computer interaction Books > Professional & Technical > Mechanical engineering & materials > Production engineering > General Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning
LSN:	3-658-39178-2
Barcode:	9783658391782

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