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dc.contributor.authorBauer, Dennis-
dc.contributor.authorUmgelter, Daniel-
dc.contributor.authorSchlereth, Andreas-
dc.contributor.authorBauernhansl, Thomas-
dc.contributor.authorSauer, Alexander-
dc.date.accessioned2023-04-20T09:34:46Z-
dc.date.available2023-04-20T09:34:46Z-
dc.date.issued2023de
dc.identifier.issn2076-3417-
dc.identifier.other1843780674-
dc.identifier.urihttp://nbn-resolving.de/urn:nbn:de:bsz:93-opus-ds-129752de
dc.identifier.urihttp://elib.uni-stuttgart.de/handle/11682/12975-
dc.identifier.urihttp://dx.doi.org/10.18419/opus-12956-
dc.description.abstractThe manufacturing industry is facing increasing volatility, uncertainty, complexity, and ambiguity, while still requiring high delivery reliability to meet customer demands. This is especially challenging for complex job shops in the semiconductor industry, where the manufacturing process is highly intricate, making it difficult to predict the consequences of changes. Although simulation has proven to be an effective tool for optimizing manufacturing processes, reference data sets and models often produce disparate and incomparable results. CoJoSim is introduced in this article as a reference model for semiconductor manufacturing, along with an associated reference implementation that accelerates the implementation and application of the reference model. CoJoSim can serve as a testbed and gold standard for other implementations. Using CoJoSim, different dispatching rules are evaluated to demonstrate an improvement of almost 15 percentage points in adherence to delivery dates compared to the reference. Findings emphasize the importance of optimizing setup time, particularly in products with high variance, as it significantly impacts adherence to delivery dates and throughput. Moving forward, future applications of CoJoSim will evaluate additional dispatching rules and use cases. Combining CoJoSim with dispatching methods that integrate manufacturing and supply networks to optimize production planning and control through reinforcement-learning-based agents is also planned. In conclusion, CoJoSim provides a reliable and effective tool for optimizing semiconductor manufacturing and can serve as a benchmark for future implementations.en
dc.description.sponsorshipElectronic Component Systems for European Leadership Joint Undertaking (ECSEL JU)de
dc.description.sponsorshipPower Semiconductor and Electronics Manufacturing 4.0 (SemI40)de
dc.language.isoende
dc.relationinfo:eu-repo/grantAgreement/EC/H2020/692466de
dc.relation.uridoi:10.3390/app13063615de
dc.rightsinfo:eu-repo/semantics/openAccessde
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/de
dc.subject.ddc670de
dc.titleComplex job shop simulation “CoJoSim” : a reference model for simulating semiconductor manufacturingen
dc.typearticlede
dc.date.updated2023-04-05T10:44:19Z-
ubs.fakultaetEnergie-, Verfahrens- und Biotechnikde
ubs.fakultaetKonstruktions-, Produktions- und Fahrzeugtechnikde
ubs.fakultaetExterne wissenschaftliche Einrichtungende
ubs.institutInstitut für Energieeffizienz in der Produktionde
ubs.institutInstitut für Industrielle Fertigung und Fabrikbetriebde
ubs.institutFraunhofer Institut für Produktionstechnik und Automatisierung (IPA)de
ubs.publikation.seiten19de
ubs.publikation.sourceApplied sciences 13 (2023), No. 3615de
ubs.publikation.typZeitschriftenartikelde
Enthalten in den Sammlungen:04 Fakultät Energie-, Verfahrens- und Biotechnik

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