Managing risk in semiconductor manufacturing: A stochastic predictive control approach

Ascensión Zafra-Cabeza, Miguel A. Ridao, Eduardo F. Camacho, Karl G. Kempf, Daniel Rivera

Research output: Contribution to journalArticlepeer-review

14 Scopus citations


The paper proposes a method to optimize the cost and time of a project. The method considers principles from risk management and applying model predictive control (MPC). The control variables (continuous or discrete) are the mitigation actions that must be executed in order to reduce risk exposure. Risk impacts are considered to be stochastic variables to model uncertainties that could potentially appear. As a consequence, a stochastic mixed integer quadratic optimization problem is obtained. Furthermore, Monte Carlo simulation is executed by considering random variables on different variables. A real-life risk management problem related to the construction of semiconductor manufacturing facilities is presented. The given solution illustrates the effectiveness of the method.

Original languageEnglish (US)
Pages (from-to)969-984
Number of pages16
JournalControl Engineering Practice
Issue number8
StatePublished - Aug 2007


  • Hybrid systems
  • Manufacturing processes
  • Predictive control
  • Project management
  • Risk
  • Stochastic programming

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Applied Mathematics


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