A novel model predictive control algorithm for supply chain management in semiconductor manufacturing

Wenlin Wang, Daniel Rivera, Karl G. Kempf

Research output: Chapter in Book/Report/Conference proceedingConference contribution

18 Scopus citations

Abstract

Supply chains in semiconductor manufacturing are characterized by integrating dynamics, nonlinearity and high levels of stochasticity. In this paper, we present a novel Model Predictive Control (MPC) algorithm for Supply Chain Management (SCM) in semiconductor manufacturing. A Type II filter is designed to attenuate the integrating noise such as that exhibited by unforecasted customer demand. The selection of the filter gain provides the flexibility to achieve better performance and robustness. The forecast of customer demand plays a critical role in the algorithm. The advantages of this novel MPC algorithm are demonstrated through case studies of a representative supply chain problem in semiconductor manufacturing which involve scenarios of customer demand forecast error and anticipated periodic demand.

Original languageEnglish (US)
Title of host publicationProceedings of the American Control Conference
Pages208-213
Number of pages6
Volume1
StatePublished - 2005
Event2005 American Control Conference, ACC - Portland, OR, United States
Duration: Jun 8 2005Jun 10 2005

Other

Other2005 American Control Conference, ACC
CountryUnited States
CityPortland, OR
Period6/8/056/10/05

ASJC Scopus subject areas

  • Control and Systems Engineering

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    Wang, W., Rivera, D., & Kempf, K. G. (2005). A novel model predictive control algorithm for supply chain management in semiconductor manufacturing. In Proceedings of the American Control Conference (Vol. 1, pp. 208-213). [WeA07.2]