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Autor(en): Bauer, Dennis
Umgelter, Daniel
Schlereth, Andreas
Bauernhansl, Thomas
Sauer, Alexander
Titel: Complex job shop simulation “CoJoSim” : a reference model for simulating semiconductor manufacturing
Erscheinungsdatum: 2023
Dokumentart: Zeitschriftenartikel
Seiten: 19
Erschienen in: Applied sciences 13 (2023), No. 3615
URI: http://nbn-resolving.de/urn:nbn:de:bsz:93-opus-ds-129752
http://elib.uni-stuttgart.de/handle/11682/12975
http://dx.doi.org/10.18419/opus-12956
ISSN: 2076-3417
Zusammenfassung: The 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.
Enthalten in den Sammlungen:04 Fakultät Energie-, Verfahrens- und Biotechnik

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