Optimal computing budget allocation with input uncertainty

Siyang Gao; Hui Xiao; Enlu Zhou; Weiwei Chen

doi:10.1109/WSC.2016.7822146

Optimal computing budget allocation with input uncertainty

Siyang Gao, Hui Xiao, Enlu Zhou, Weiwei Chen

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

11 Citations (Scopus)

Abstract

In this study, we consider ranking and selection problems where the simulation model is subject to input uncertainty. Under the input uncertainty, we compare system designs based on their worst-case performance, and seek to maximize the probability of selecting the design with the best performance under the worst-case scenario. By approximating the probability of correct selection (PCS), we develop an asymptotically (as the simulation budget goes to infinity) optimal solution of the resulting problem. An efficient selection procedure is designed within the optimal computing budget allocation (OCBA) framework. Numerical tests show the high efficiency of the proposed method.

Original language	English
Title of host publication	Proceedings - Winter Simulation Conference
Publisher	IEEE
Pages	839-846
ISBN (Print)	9781509044863
DOIs	https://doi.org/10.1109/WSC.2016.7822146
Publication status	Published - 17 Jan 2017
Event	2016 Winter Simulation Conference, WSC 2016 - Arlington, United States Duration: 11 Dec 2016 → 14 Dec 2016 https://informs-sim.org/wsc16papers/by_area.html http://meetings2.informs.org/wordpress/wintersim2016/

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Name
ISSN (Print)	0891-7736

Conference

Conference	2016 Winter Simulation Conference, WSC 2016
Place	United States
City	Arlington
Period	11/12/16 → 14/12/16
Internet address	https://informs-sim.org/wsc16papers/by_area.html http://meetings2.informs.org/wordpress/wintersim2016/

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abstract = "In this study, we consider ranking and selection problems where the simulation model is subject to input uncertainty. Under the input uncertainty, we compare system designs based on their worst-case performance, and seek to maximize the probability of selecting the design with the best performance under the worst-case scenario. By approximating the probability of correct selection (PCS), we develop an asymptotically (as the simulation budget goes to infinity) optimal solution of the resulting problem. An efficient selection procedure is designed within the optimal computing budget allocation (OCBA) framework. Numerical tests show the high efficiency of the proposed method.",

author = "Siyang Gao and Hui Xiao and Enlu Zhou and Weiwei Chen",

year = "2017",

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doi = "10.1109/WSC.2016.7822146",

language = "English",

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publisher = "IEEE",

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booktitle = "Proceedings - Winter Simulation Conference",

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AU - Xiao, Hui

AU - Zhou, Enlu

AU - Chen, Weiwei

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N2 - In this study, we consider ranking and selection problems where the simulation model is subject to input uncertainty. Under the input uncertainty, we compare system designs based on their worst-case performance, and seek to maximize the probability of selecting the design with the best performance under the worst-case scenario. By approximating the probability of correct selection (PCS), we develop an asymptotically (as the simulation budget goes to infinity) optimal solution of the resulting problem. An efficient selection procedure is designed within the optimal computing budget allocation (OCBA) framework. Numerical tests show the high efficiency of the proposed method.

AB - In this study, we consider ranking and selection problems where the simulation model is subject to input uncertainty. Under the input uncertainty, we compare system designs based on their worst-case performance, and seek to maximize the probability of selecting the design with the best performance under the worst-case scenario. By approximating the probability of correct selection (PCS), we develop an asymptotically (as the simulation budget goes to infinity) optimal solution of the resulting problem. An efficient selection procedure is designed within the optimal computing budget allocation (OCBA) framework. Numerical tests show the high efficiency of the proposed method.

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DO - 10.1109/WSC.2016.7822146

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 9781509044863

SP - 839

EP - 846

BT - Proceedings - Winter Simulation Conference

PB - IEEE

T2 - 2016 Winter Simulation Conference, WSC 2016

Y2 - 11 December 2016 through 14 December 2016

ER -

Optimal computing budget allocation with input uncertainty

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