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

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Generic Multi-Agent Reinforcement Learning Approach for Flexible Job-Shop Scheduling/ by Schirin Bär.
Author:	Bär, Schirin.
Description:	XXII, 148 p. 39 illus., 35 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Machine learning. -
Online resource:	https://doi.org/10.1007/978-3-658-39179-9
ISBN:	9783658391799

Generic Multi-Agent Reinforcement Learning Approach for Flexible Job-Shop Scheduling
Bär, Schirin.

Generic Multi-Agent Reinforcement Learning Approach for Flexible Job-Shop Scheduling[electronic resource] /by Schirin Bär. - 1st ed. 2022. - XXII, 148 p. 39 illus., 35 illus. in color.online resource.

Introduction -- Requirements for Production Scheduling in Flexible Manufacturing -- Reinforcement Learning as an Approach for Flexible Scheduling -- Concept for Multi-Resources Flexible Job-Shop Scheduling -- Multi-Agent Approach for Reactive Scheduling in Flexible Manufacturing -- Empirical Evaluation of the Requirements -- Integration into a Flexible Manufacturing System -- Bibliography.

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. 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.

ISBN: 9783658391799

Standard No.: 10.1007/978-3-658-39179-9doiSubjects--Topical Terms:

561253
Machine learning.

LC Class. No.: Q325.5-.7

Dewey Class. No.: 006.31

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