| Application of combined discrete-event simulation and optimization models in semiconductor enterprise manufacturing systems |
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Winter Simulation Conference
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Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
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Washington D.C.
SESSION: Semiconductor manufacturing: semiconductor manufacturing performance improvement
table of contents
Pages 1729-1736
Year of Publication: 2007
ISBN:1-4244-1306-0
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IEEE Press
Piscataway, NJ, USA
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Downloads (6 Weeks): 4, Downloads (12 Months): 34, Citation Count: 0
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ABSTRACT
It is a common practice to use simulation for validating different types of control and planning algorithms. However, the science of how to rigorously integrate simulation and decision models is not well understood and becomes critically important as the complexity and scale of these models increase. In our research, we have developed a methodology for integrating different types of models using a Knowledge Interchange Broker (KIB). In this paper we describe a supply-chain semiconductor application where the KIB has been used as an integral part of developing and deploying a commercial Model Predictive Control model for use in operating a multi-billion dollar supply chain. The simulation based experiments facilitated developing and validating the controller design and data automation for a real-world semiconductor manufacturing system.
REFERENCES
Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.
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Huang, D., H. Sarjoughian, D. Rivera, G. Godding, K. Kempf. 2006. Experiment Analysis of Hybrid Discrete Event Simulation with Model Predictive Control for Semiconductor Supply Chain Systems, In Proceedings of Winter Simulation Conference, 1863--1870. Monterey, CA, USA.
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K. Kempf. 2004. Control-Oriented Approaches to Supply Chain Management in Semiconductor Manufacturing. Proceeding of the American Control Conference, 4563--4576. Boston, MA, USA.
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Qin, S., and T. Badgwell. 2003. A survey of industrial model predictive control technology. Control Engineering Practice 11 (7): 733--764.
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