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

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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 languageEnglish
Title of host publication2017 Winter Simulation Conference, WSC 2017
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
Number of pages12
ISBN (Electronic)9781538634288
ISBN (Print)9781538634271
Publication statusPublished - 4 Jan 2018
Event2017 Winter Simulation Conference (WSC 2017) - Las Vegas, United States
Duration: 3 Dec 20176 Dec 2017


Conference2017 Winter Simulation Conference (WSC 2017)
Abbreviated titleWSC 2017
CountryUnited States
CityLas Vegas
Internet address

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