Marvin Carl May

Intelligent production control for time-constrained complex job shops


Front	Back

ISBN:

978-3-8440-9464-0

Series:

Forschungsberichte aus dem wbk, Institut für Produktionstechnik, Karlsruher Institut für Technologie (KIT)
Herausgeber: Prof. Dr.-Ing. Jürgen Fleischer, Prof. Dr.-Ing. Gisela Lanza and Prof. Dr.-Ing. habil. Volker Schulze
Karlsruhe

Volume:

278

Keywords:

Semiconductor Manufacturing; Complex Job Shops; Production Planning and Control; Intelligent Production Control

Type of publication:

Thesis

Language:

English

Pages:

250 pages

Figures:

67 figures

Weight:

338 g

Format:

21 x 14,8 cm

Binding:

Paperback

Price:

59,80 € / 74,80 SFr

Published:

April 2024

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Abstract:

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.