### Samenvatting

We consider an engineer-to-order production system with unknown yield. We model the yield as a random variable which represents the percentage output obtained from one unit of production quantity. We develop a beta-regression model in which the mean value of the yield depends on the unique attributes of the engineer-to-order product. Assuming that the beta-regression parameters are unknown by the decision maker, we investigate the problem of identifying the optimal production quantity. Adopting a Bayesian approach for modeling the uncertainty in the beta-regression parameters, we use simulation to approximate the posterior distributions of these parameters. We further quantify the increase in the expected cost due to the so-called input uncertainty as a function of the size of the historical data set, the product attributes, and economic parameters. We also introduce a sampling-based algorithm that reduces the average increase in the expected cost due to input uncertainty.

Originele taal-2 | Engels |
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Titel | 2017 Winter Simulation Conference, WSC 2017 |

Plaats van productie | Piscataway |

Uitgeverij | Institute of Electrical and Electronics Engineers |

Pagina's | 3275-3286 |

Aantal pagina's | 12 |

ISBN van elektronische versie | 9781538634288 |

ISBN van geprinte versie | 9781538634271 |

DOI's | |

Status | Gepubliceerd - 4 jan 2018 |

Evenement | 2017 Winter Simulation Conference (WSC 2017) - Las Vegas, Verenigde Staten van Amerika Duur: 3 dec 2017 → 6 dec 2017 http://meetings2.informs.org/wordpress/wsc2017/ |

### Congres

Congres | 2017 Winter Simulation Conference (WSC 2017) |
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Verkorte titel | WSC 2017 |

Land | Verenigde Staten van Amerika |

Stad | Las Vegas |

Periode | 3/12/17 → 6/12/17 |

Internet adres |

### Vingerafdruk

### Citeer dit

*2017 Winter Simulation Conference, WSC 2017*(blz. 3275-3286). Piscataway: Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/WSC.2017.8248045