Optimal operation of semiconductor manufacturing supply chains under uncertainty using simulation-based optimization

Jay Schwartz, Daniel Rivera, Karl G. Kempf

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


Efficient management of inventory in supply chains is critical to the profitable operation of modern enterprises. The supply/demand networks characteristic of discrete-parts industries such as semiconductor manufacturing represent highly stochastic, nonlinear, and constrained dynamical systems whose study merits a control-oriented approach. Model Predictive Control (MPC) is presented in this paper as the basis for a novel inventory management policy for supply chains whose dynamic behavior can be adequately represented by fluid analogies. A Simultaneous Perturbation Stochastic Approximation (SPSA) optimization algorithm is presented as a means to obtain optimal tuning parameters for the proposed policies. The SPSA technique is capable of optimizing important system parameters, such as safety stock targets and/or controller tuning parameters. Two case studies are presented. The results of the optimization on a single-echelon system show that it is advantageous to act cautiously to forecasted information and gradually become more aggressive (with respect to factory starts) as more accurate demand information becomes available. For a three-echelon problem, the results of the optimization demonstrate that safety stock levels can be significantly reduced and financial benefit gained while maintaining robust operation in the supply chain.

Original languageEnglish (US)
Title of host publicationAIChE Annual Meeting, Conference Proceedings
Number of pages12
StatePublished - 2005
Event05AIChE: 2005 AIChE Annual Meeting and Fall Showcase - Cincinnati, OH, United States
Duration: Oct 30 2005Nov 4 2005


Other05AIChE: 2005 AIChE Annual Meeting and Fall Showcase
Country/TerritoryUnited States
CityCincinnati, OH

ASJC Scopus subject areas

  • General Engineering


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