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E-book
Author Bär, Schirin, author

Title Generic multi-agent reinforcement learning approach for flexible job-shop scheduling / Schirin Bär
Published Wiesbaden, Germany : Springer Vieweg, 2022

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Description 1 online resource (xxii, 148 pages) : illustrations (some color)
Contents 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
Summary 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
Bibliography Includes bibliographical references
Notes Abtracts in German and English
Online resource; title from PDF title page (SpringerLink, viewed October 10, 2022)
Subject Reinforcement learning.
Multiagent systems.
Flexible manufacturing systems.
Aprendizaje automático (Inteligencia artificial)
Flexible manufacturing systems
Multiagent systems
Reinforcement learning
Form Electronic book
ISBN 9783658391799
3658391790