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dc.contributor.authorLohse, Oliver
dc.date.accessioned2025-03-07T13:14:07Z
dc.date.available2025-03-07T13:14:07Z
dc.date.issued2023
dc.date.submitted2023-04-24T11:23:57Z
dc.identifierhttps://library.oapen.org/handle/20.500.12657/62536
dc.identifier.urihttps://doab-dev.siscern.org/handle/20.500.12854/151868
dc.description.abstractThis 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.
dc.languageGerman
dc.relation.ispartofseriesReihe Informationsmanagement im Engineering Karlsruhe
dc.rightsopen access
dc.subject.classificationthema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TG Mechanical engineering and materials
dc.subject.otherProduktionssteuerung; Reinforcement Learning; Künstliche Intelligenz; Terminierung; Production control; artificial intelligence; scheduling
dc.titleEntwicklung einer Methode zum Einsatz von Reinforcement Learning für die dynamische Fertigungsdurchlaufsteuerung
dc.typebook
oapen.identifier.doi10.5445/KSP/1000156002
oapen.relation.isPublishedBy68fffc18-8f7b-44fa-ac7e-0b7d7d979bd2
oapen.relation.isbn9783731512820
oapen.collectionAG Universitätsverlage
oapen.pages208
peerreview.review.typeFull text
peerreview.anonymityAll identities known
peerreview.reviewer.typeEditorial board member
peerreview.reviewer.typeExternal peer reviewer
peerreview.review.stagePre-publication
peerreview.open.reviewNo
peerreview.publish.responsibilityBooks or series editor
peerreview.id51a542ec-eaeb-47c2-861d-6022e981a97a
dc.seriesnumber25
peerreview.titleDissertations in Series (Dissertationen in Schriftenreihe)


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