التفاصيل البيبلوغرافية
| العنوان: |
Managing product-inherent constraints with artificial intelligence: production control for time constraints in semiconductor manufacturing. |
| المؤلفون: |
May, Marvin Carl1 (AUTHOR) marvin.may@kit.edu, Oberst, Jan1 (AUTHOR), Lanza, Gisela1 (AUTHOR) |
| المصدر: |
Journal of Intelligent Manufacturing. Dec2024, Vol. 35 Issue 8, p4259-4276. 18p. |
| مصطلحات موضوعية: |
*PRODUCTION control, *ECONOMIC lot size, *JOB shops, *ARTIFICIAL intelligence, CONSTRAINT satisfaction |
| مستخلص: |
Continuous product individualization and customization led to the advent of lot size one in production and ultimately to product-inherent uniqueness. As complexities in individualization and processes grow, production systems need to adapt to unique, product-inherent constraints by advancing production control beyond predictive, rigid schedules. While complex processes, production systems and production constraints are not a novelty per se, modern production control approaches fall short of simultaneously regarding the flexibility of complex job shops and product unique constraints imposed on production control. To close this gap, this paper develops a novel, data driven, artificial intelligence based production control approach for complex job shops. For this purpose, product-inherent constraints are resolved by restricting the solution space of the production control according to a prediction based decision model. The approach validation is performed in a real semiconductor fab as a job shop that includes transitional time constraints as product-inherent constraints. Not violating these time constraints is essential to avoid scrap and similarly increase quality-based yield. To that end, transition times are forecasted and the adherence to these product-inherent constraints is evaluated based on one-sided prediction intervals and point estimators. The inclusion of product-inherent constraints leads to significant adherence improvements in the production system as indicated in the real-world semiconductor manufacturing case study and, hence, contributes a novel, data driven approach for production control. As a conclusion, the ability to avoid a large majority of violations of time constraints shows the approaches effectiveness and the future requirement to more accurately integrate such product-inherent constraints into production control. [ABSTRACT FROM AUTHOR] |
|
Copyright of Journal of Intelligent Manufacturing is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| قاعدة البيانات: |
Business Source Index |