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Intelligent production control for time-constrained complex job shops

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Produktnummer: 1825aecad234cd4a2792891338ca146a2a
Autor: May, Marvin Carl
Themengebiete: Complex Job Shops Intelligent Production Control Production Planning and Control Semiconductor Manufacturing
Veröffentlichungsdatum: 16.05.2024
EAN: 9783844094640
Auflage: 1
Sprache: Englisch
Seitenzahl: 217
Produktart: Kartoniert / Broschiert
Verlag: Shaker
Produktinformationen "Intelligent production control for time-constrained complex job shops"
In wake of an ever increasing complexity the desire to move towards intelligently controlling operations is amplified in manufacturing. Complex job shops mark the most complex production environments that require a high degree of agility to control. Among these complex manufacturing environments semiconductor manufacturing stands out as it combines all complexities to form a truly complex job shop. Hence, operational excellence is the key to success and relies on intelligent production control. A major concern in controlling such complex job shops, in this case semiconductor wafer fabrication, is the presence of time-constraints that limit the transition time of products between two, mostly successive, processes. Adhering to these product specific time-constraints is of utmost importance as violations result in scrapping the violating product. The state-of-the-art production control of these dispatching decisions that aim at adhering to time-constraints is based on error-prone manual control that is stressful for human operators. Thus, within this thesis a novel, real-time data based approach for intelligently controlling production control for time-constrained complex job shops is presented. Using an up-to-the-minute replica of the real system both uni-, multi-variate time series models and a digital twin are used to obtain violation predictions. As a second step, based on the time-constraint violation expectancy the production control is derived and implemented with a real-world semiconductor manufacturing plant real-time data. The resulting approach is, therefore, validated against the state-of-the-art showing significant improvements as many time-constraint violations could be prevented.
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