## Abstract

Economic statistical designs aim at minimizing the cost of process monitoring when a specific scenario or a set of estimated process and cost parameters is given. But, in practice the process may be affected by more than one scenario which may lead to severe cost penalties if the wrong design is used. Here, we investigate the robust economic statistical design (RESD) of the T^{2} chart in an attempt to reduce these cost penalties when there are multiple scenarios. Our method is to employ the genetic algorithm (GA) optimization method to minimize the total expected monitoring cost across all distinct scenarios. We illustrate the effectiveness of the method using two numerical examples. Simulation studies indicate that robust economic statistical designs should be encouraged in practice.

Original language | English |
---|---|

Pages (from-to) | 6989-7001 |

Number of pages | 13 |

Journal | COMMUNICATIONS IN STATISTICS-THEORY AND METHODS |

Volume | 45 |

Issue number | 23 |

DOIs | |

Publication status | Published - 1 Dec 2016 |

## Keywords

- Economic statistical design
- Genetic algorithm
- Hotelling's T control chart
- Robust design