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 journalArticle

12 Citations (Scopus)

Abstract

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
Volume15
Issue number8
DOIs
StatePublished - Aug 2007

Fingerprint

Semiconductor Manufacturing
Predictive Control
Stochastic Control
Risk management
Risk Management
Semiconductor materials
Model predictive control
Random variables
Quadratic Optimization
Discrete Variables
Model Predictive Control
Continuous Variables
Model Uncertainty
Monte Carlo Simulation
Random variable
Optimise
Optimization Problem
Costs
Integer
Uncertainty

Keywords

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

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering

Cite this

Managing risk in semiconductor manufacturing : A stochastic predictive control approach. / Zafra-Cabeza, Ascensión; Ridao, Miguel A.; Camacho, Eduardo F.; Kempf, Karl G.; Rivera, Daniel.

In: Control Engineering Practice, Vol. 15, No. 8, 08.2007, p. 969-984.

Research output: Contribution to journalArticle

Zafra-Cabeza, Ascensión ; Ridao, Miguel A. ; Camacho, Eduardo F. ; Kempf, Karl G. ; Rivera, Daniel. / Managing risk in semiconductor manufacturing : A stochastic predictive control approach. In: Control Engineering Practice. 2007 ; Vol. 15, No. 8. pp. 969-984.
@article{f7ef2110615d48dfb0567acca98c9a96,
title = "Managing risk in semiconductor manufacturing: A stochastic predictive control approach",
abstract = "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.",
keywords = "Hybrid systems, Manufacturing processes, Predictive control, Project management, Risk, Stochastic programming",
author = "Ascensi{\'o}n Zafra-Cabeza and Ridao, {Miguel A.} and Camacho, {Eduardo F.} and Kempf, {Karl G.} and Daniel Rivera",
year = "2007",
month = "8",
doi = "10.1016/j.conengprac.2006.12.004",
language = "English (US)",
volume = "15",
pages = "969--984",
journal = "Control Engineering Practice",
issn = "0967-0661",
publisher = "Elsevier Limited",
number = "8",

}

TY - JOUR

T1 - Managing risk in semiconductor manufacturing

T2 - A stochastic predictive control approach

AU - Zafra-Cabeza, Ascensión

AU - Ridao, Miguel A.

AU - Camacho, Eduardo F.

AU - Kempf, Karl G.

AU - Rivera, Daniel

PY - 2007/8

Y1 - 2007/8

N2 - 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.

AB - 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.

KW - Hybrid systems

KW - Manufacturing processes

KW - Predictive control

KW - Project management

KW - Risk

KW - Stochastic programming

UR - http://www.scopus.com/inward/record.url?scp=34247634602&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=34247634602&partnerID=8YFLogxK

U2 - 10.1016/j.conengprac.2006.12.004

DO - 10.1016/j.conengprac.2006.12.004

M3 - Article

AN - SCOPUS:34247634602

VL - 15

SP - 969

EP - 984

JO - Control Engineering Practice

JF - Control Engineering Practice

SN - 0967-0661

IS - 8

ER -