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Technology Readiness Levels of Reinforcement Learning methods for simulation-based production scheduling

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dc.contributor.authorSeipolt, Arne
dc.contributor.authorBuschermöhle, Ralf
dc.contributor.authorHöfinghoff, Maximilian
dc.contributor.authorKorn, Goy-Hinrich
dc.contributor.authorSchumacher, Marcel
dc.contributor.editorKlein, Maike
dc.contributor.editorKrupka, Daniel
dc.contributor.editorWinter, Cornelia
dc.contributor.editorWohlgemuth, Volker
dc.date.accessioned2023-11-29T14:50:20Z
dc.date.available2023-11-29T14:50:20Z
dc.date.issued2023
dc.description.abstractDigital Twins (DT) are nowadays widely used and provide a benefit for the companies using it. One service of the DT is the simulation of a production process. This enables an optimization of the production process by simulation optimization, for example with Reinforcement Learning (RL). To support researchers and practitioners in deciding which algorithm is suitable for an implementation under real-life conditions, a literature research is performed, and a Machine Learning Technology Readiness Level is assigned to the different RL-Algorithms. It can be shown that recent research focuses mainly on model free value based and evolutionary algorithms, and both are suitable for an implementation in a real-world scenario. Both algorithms can outperform widely applied dispatching rules. Nevertheless, it should be evaluated why other algorithms are not in the focus of recent research and how the algorithms perform in comparison to each other.en
dc.identifier.doi10.18420/inf2023_144
dc.identifier.isbn978-3-88579-731-9
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/43068
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofINFORMATIK 2023 - Designing Futures: Zukünfte gestalten
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-337
dc.subjectTechnology Readiness
dc.subjectReinforcement Learning
dc.subjectSimulation
dc.subjectProduction Scheduling
dc.titleTechnology Readiness Levels of Reinforcement Learning methods for simulation-based production schedulingen
dc.typeText/Conference Paper
gi.citation.endPage1390
gi.citation.publisherPlaceBonn
gi.citation.startPage1375
gi.conference.date26.-29. September 2023
gi.conference.locationBerlin
gi.conference.sessiontitleÖkologische Nachhaltigkeit - Zukunft nachhaltig gestalten durch digitalisierte Wertschöpfungsprozesse (DigiWe)

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P337 - INFORMATIK 2023 - Designing Futures: Zukünfte gestalten