Articoli correlati a Generic Multi-Agent Reinforcement Learning Approach...

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

Bär, Schirin

 
9783658391782: Generic Multi-Agent Reinforcement Learning Approach for Flexible Job-Shop Scheduling
Brossura
ISBN 10: 3658391782 ISBN 13: 9783658391782
Casa editrice: Springer Vieweg, 2022

Sinossi

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.


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Informazioni sull'autore


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.

Dalla quarta di copertina

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.

Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.

Editore
Springer Vieweg
Data di pubblicazione
2022
Lingua
Inglese
ISBN 10 
3658391782
ISBN 13 
9783658391782
Rilegatura
Copertina flessibile
Numero edizione
1
Numero di pagine
172
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