Stochastic simulation under input uncertainty for contract manufacturer selection in pharmaceutical industry

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3 Citaten (Scopus)
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Samenvatting

We consider a pharmaceutical company that sources a biological product from a set of unreliable contract manufacturers. The likelihood of a manufacturer to successfully deliver the product is estimated via logistic regression as a function of the product attributes. The assignment of a product to the right contract manufacturers is of critical importance for the pharmaceutical company, and simulation-based optimization is used to identify the optimal sourcing decision. However, the input uncertainty due to the uncertain parameters of the logistic regression model often leads to poor sourcing decisions. We quantify the decrease in the expected profit due to input uncertainty as a function of the size of the historical data set, the level of dispersion in the historical data of a product attribute, and the number of products. We also introduce a sampling-based algorithm that reduces the expected decrease in the expected profit.
Originele taal-2Engels
TitelProceedings of the 2016 Winter Simulation Conference
RedacteurenT.M.K Roeder, P.I. Frazier, R. Szechtman, E. Zhou, T. Huschka, S.E. Chick
Plaats van productieWashington, D.C.
UitgeverijINFORMS Institute for Operations Research and the Management Sciences
Pagina's2292-2303
Aantal pagina's12
DOI's
StatusGepubliceerd - 2016
Evenement2016 Winter Simulation Conference (WSC 2016) - Washington, D.C., Washington, Verenigde Staten van Amerika
Duur: 11 dec 201614 dec 2016
http://informs-sim.org/wsc16papers/by_area.html

Congres

Congres2016 Winter Simulation Conference (WSC 2016)
Verkorte titelWSC 2016
LandVerenigde Staten van Amerika
StadWashington
Periode11/12/1614/12/16
Internet adres

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Citeer dit

Akçay, A. E., & Martagan, T. G. (2016). Stochastic simulation under input uncertainty for contract manufacturer selection in pharmaceutical industry. In T. M. K. Roeder, P. I. Frazier, R. Szechtman, E. Zhou, T. Huschka, & S. E. Chick (editors), Proceedings of the 2016 Winter Simulation Conference (blz. 2292-2303). Washington, D.C.: INFORMS Institute for Operations Research and the Management Sciences. https://doi.org/10.1109/WSC.2016.7822270