Generic Multi-Agent Reinforcement Learning Approach for Flexible Job-Shop Scheduling

Bär, Schirin

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.

Details

Published by: Springer Vieweg

Publication Date: 2022-10-02

Format: Paperback

ISBN-13: 9783658391782

DOI: 10.1007/978-3-658-39179-9

Dimensions: 210cm x148cm

Pages: 148