### Abstract

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.

Original language | English |
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Title of host publication | 2017 Winter Simulation Conference, WSC 2017 |

Place of Publication | Piscataway |

Publisher | Institute of Electrical and Electronics Engineers |

Pages | 3275-3286 |

Number of pages | 12 |

ISBN (Electronic) | 9781538634288 |

ISBN (Print) | 9781538634271 |

DOIs | |

Publication status | Published - 4 Jan 2018 |

Event | 2017 Winter Simulation Conference (WSC 2017) - Las Vegas, United States Duration: 3 Dec 2017 → 6 Dec 2017 http://meetings2.informs.org/wordpress/wsc2017/ |

### Conference

Conference | 2017 Winter Simulation Conference (WSC 2017) |
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Abbreviated title | WSC 2017 |

Country | United States |

City | Las Vegas |

Period | 3/12/17 → 6/12/17 |

Internet address |

### Fingerprint

### Cite this

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

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*2017 Winter Simulation Conference, WSC 2017.*Institute of Electrical and Electronics Engineers, Piscataway, pp. 3275-3286, 2017 Winter Simulation Conference (WSC 2017), Las Vegas, United States, 3/12/17. https://doi.org/10.1109/WSC.2017.8248045

**Simulation-based production planning for engineer-to-order systems with random yield.** / Akcay, Alp; Martagan, Tugce.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Simulation-based production planning for engineer-to-order systems with random yield

AU - Akcay, Alp

AU - Martagan, Tugce

PY - 2018/1/4

Y1 - 2018/1/4

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85044541078&partnerID=8YFLogxK

U2 - 10.1109/WSC.2017.8248045

DO - 10.1109/WSC.2017.8248045

M3 - Conference contribution

AN - SCOPUS:85044541078

SN - 9781538634271

SP - 3275

EP - 3286

BT - 2017 Winter Simulation Conference, WSC 2017

PB - Institute of Electrical and Electronics Engineers

CY - Piscataway

ER -