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Multi-objective Optimisation Using Evolutionary Algorithms: An Introduction Read chapter Read first chapter

2011 | OriginalPaper | Chapter

11. Distributed Real-Time Scheduling by Using Multi-agent Reinforcement Learning

Authors : Koji Iwamura, Nobuhiro Sugimura

Published in: Multi-objective Evolutionary Optimisation for Product Design and Manufacturing

Publisher: Springer London

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Abstract

Autonomous Distributed Manufacturing Systems (ADMS) have been proposed to realise flexible control structures of manufacturing systems. In the previous researches, a real-time scheduling method based on utility values has been proposed and applied to the ADMS. Multi-agent reinforcement learning is newly proposed and implemented to the job agents and resource agents, in order to improve their coordination processes. The status, the action and the reward are defined for the individual job agents and the resource agents to evaluate the suitable utility values based on the status of the ADMS. Some case studies of the real-time scheduling have been carried out to verify the effectiveness of the proposed methods.

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previous chapter Intelligent Optimisation for Integrated Process Planning and Scheduling
next chapter A Multiple Ant Colony Optimisation Approach for a Multi-objective Manufacturing Rescheduling Problem
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Metadata
Title
Distributed Real-Time Scheduling by Using Multi-agent Reinforcement Learning
Authors
Koji Iwamura
Nobuhiro Sugimura
Copyright Year
2011
Publisher
Springer London
Book
Multi-objective Evolutionary Optimisation for Product Design and Manufacturing

Print ISBN: 978-0-85729-617-7

Electronic ISBN: 978-0-85729-652-8

Copyright Year: 2011

DOI
https://doi.org/10.1007/978-0-85729-652-8_11

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