A note on the subset selection for simulation optimization

Siyang Gao; Weiwei Chen

doi:10.1109/WSC.2015.7408534

A note on the subset selection for simulation optimization

Siyang Gao, Weiwei Chen

Rutgers Business School, Supply Chain Management

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

11 Scopus citations

Abstract

In this paper, we consider the problem of selecting an optimal subset from a finite set of simulated designs. Using the optimal computing budget allocation (OCBA) framework, we formulate the problem as that of maximizing the probability of correctly selecting the top m designs subject to a constraint on the total number of samples available. For an approximation of the probability of correct selection, we derive an asymptotically optimal subset selection procedure that is easy to implement. More importantly, we provide some useful insights on characterizing an efficient subset selection rule and how it can be achieved by adjusting the budgets allocated to the optimal and non-optimal subsets.

Original language	English (US)
Title of host publication	2015 Winter Simulation Conference, WSC 2015
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	3768-3776
Number of pages	9
ISBN (Electronic)	9781467397438
DOIs	https://doi.org/10.1109/WSC.2015.7408534
State	Published - Feb 16 2016
Event	Winter Simulation Conference, WSC 2015 - Huntington Beach, United States Duration: Dec 6 2015 → Dec 9 2015

Publication series

Name	Proceedings - Winter Simulation Conference
Volume	2016-February
ISSN (Print)	0891-7736

Other

Other	Winter Simulation Conference, WSC 2015
Country/Territory	United States
City	Huntington Beach
Period	12/6/15 → 12/9/15

All Science Journal Classification (ASJC) codes

Software
Modeling and Simulation
Computer Science Applications

Access to Document

10.1109/WSC.2015.7408534

Cite this

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title = "A note on the subset selection for simulation optimization",

abstract = "In this paper, we consider the problem of selecting an optimal subset from a finite set of simulated designs. Using the optimal computing budget allocation (OCBA) framework, we formulate the problem as that of maximizing the probability of correctly selecting the top m designs subject to a constraint on the total number of samples available. For an approximation of the probability of correct selection, we derive an asymptotically optimal subset selection procedure that is easy to implement. More importantly, we provide some useful insights on characterizing an efficient subset selection rule and how it can be achieved by adjusting the budgets allocated to the optimal and non-optimal subsets.",

author = "Siyang Gao and Weiwei Chen",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; Winter Simulation Conference, WSC 2015 ; Conference date: 06-12-2015 Through 09-12-2015",

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doi = "10.1109/WSC.2015.7408534",

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Gao, S & Chen, W 2016, A note on the subset selection for simulation optimization. in 2015 Winter Simulation Conference, WSC 2015., 7408534, Proceedings - Winter Simulation Conference, vol. 2016-February, Institute of Electrical and Electronics Engineers Inc., pp. 3768-3776, Winter Simulation Conference, WSC 2015, Huntington Beach, United States, 12/6/15. https://doi.org/10.1109/WSC.2015.7408534

A note on the subset selection for simulation optimization. / Gao, Siyang; Chen, Weiwei.
2015 Winter Simulation Conference, WSC 2015. Institute of Electrical and Electronics Engineers Inc., 2016. p. 3768-3776 7408534 (Proceedings - Winter Simulation Conference; Vol. 2016-February).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AB - In this paper, we consider the problem of selecting an optimal subset from a finite set of simulated designs. Using the optimal computing budget allocation (OCBA) framework, we formulate the problem as that of maximizing the probability of correctly selecting the top m designs subject to a constraint on the total number of samples available. For an approximation of the probability of correct selection, we derive an asymptotically optimal subset selection procedure that is easy to implement. More importantly, we provide some useful insights on characterizing an efficient subset selection rule and how it can be achieved by adjusting the budgets allocated to the optimal and non-optimal subsets.

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A note on the subset selection for simulation optimization

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