### 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 language | English (US) |
---|---|

Pages (from-to) | 969-984 |

Number of pages | 16 |

Journal | Control Engineering Practice |

Volume | 15 |

Issue number | 8 |

DOIs | |

State | Published - Aug 2007 |

### Fingerprint

### 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

*Control Engineering Practice*,

*15*(8), 969-984. https://doi.org/10.1016/j.conengprac.2006.12.004

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

Research output: Contribution to journal › Article

*Control Engineering Practice*, vol. 15, no. 8, pp. 969-984. https://doi.org/10.1016/j.conengprac.2006.12.004

}

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 -