Entwicklung einer Methode zum Einsatz von Reinforcement Learning für die dynamische Fertigungsdurchlaufsteuerung

Lohse, Oliver

doi:10.5445/KSP/1000156002

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https://library.oapen.org/bitstream/20.500.12657/62536/1/entwicklung-einer-methode-zum-einsatz-von-reinforcement-learning-fur-die-dynamische-fertigungsdurchlaufsteuerung.pdf
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https://library.oapen.org/bitstream/20.500.12657/62536/1/entwicklung-einer-methode-zum-einsatz-von-reinforcement-learning-fur-die-dynamische-fertigungsdurchlaufsteuerung.pdf
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https://library.oapen.org/bitstream/20.500.12657/62536/1/entwicklung-einer-methode-zum-einsatz-von-reinforcement-learning-fur-die-dynamische-fertigungsdurchlaufsteuerung.pdf

Author(s)

Lohse, Oliver

Language

German

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Abstract

This work aims to develop a method that can reschedule the matrix production in the case of a disruption. For this purpose, different artificial intelligence methods are combined in a novel way. The developed method is validated on a theoretical and a real scheduling case.

URI

https://directory.doabooks.org/handle/20.500.12854/99534

Keywords

Produktionssteuerung; Reinforcement Learning; Künstliche Intelligenz; Terminierung; Production control; artificial intelligence; scheduling; thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TG Mechanical engineering and materials

Publication date and place

2023

Series

Reihe Informationsmanagement im Engineering Karlsruhe,

Pages

208

Rights

https://creativecommons.org/licenses/by-sa/4.0/

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Except where otherwise noted, this item's license is described as open access