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Generic multi-agent reinforcement le...

Bar, Schirin.

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Generic multi-agent reinforcement learning approach for flexible job-shop scheduling

Record Type:	Electronic resources : Monograph/item
Title/Author:	Generic multi-agent reinforcement learning approach for flexible job-shop scheduling/ by Schirin Bär.
Author:	Bar, Schirin.
Published:	Wiesbaden :Springer Fachmedien Wiesbaden : : 2022.,
Description:	xxii, 148 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Reinforcement 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
Bar, Schirin.

Generic multi-agent reinforcement learning approach for flexible job-shop scheduling[electronic resource] /by Schirin Bär. - Wiesbaden :Springer Fachmedien Wiesbaden :2022. - xxii, 148 p. :ill. (some col.), digital ;24 cm.

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:

1006373
Reinforcement learning.

LC Class. No.: Q325.6 / .B2 2022

Dewey Class. No.: 006.31

Generic multi-agent reinforcement learning approach for flexible job-shop scheduling
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505 0 $a 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.
520 $a 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.
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