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A new budget allocation framework for selecting top simulated designs

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  • Siyang Gao
  • Weiwei Chen

Abstract

In this article, the problem of selecting an optimal subset from a finite set of simulated designs is considered. Given the total simulation budget constraint, the selection problem aims to maximize the Probability of Correct Selection (PCS) of the top m designs. To simplify the complexity of the PCS, an approximated probability measure is developed and an asymptotically optimal solution of the resulting problem is derived. A subset selection procedure, which is easy to implement in practice, is then designed. More important, we provide some useful insights on characterizing an efficient subset selection rule and how it can be achieved by adjusting the simulation budgets allocated to all of the designs.

Suggested Citation

  • Siyang Gao & Weiwei Chen, 2016. "A new budget allocation framework for selecting top simulated designs," IISE Transactions, Taylor & Francis Journals, vol. 48(9), pages 855-863, September.
    • Handle: RePEc:taf:uiiexx:v:48:y:2016:i:9:p:855-863
    DOI: 10.1080/0740817X.2016.1156788
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    Cited by:

    1. Gongbo Zhang & Yijie Peng & Jianghua Zhang & Enlu Zhou, 2023. "Asymptotically Optimal Sampling Policy for Selecting Top- m Alternatives," INFORMS Journal on Computing, INFORMS, vol. 35(6), pages 1261-1285, November.
    2. Zhongshun Shi & Siyang Gao & Hui Xiao & Weiwei Chen, 2019. "A worst‐case formulation for constrained ranking and selection with input uncertainty," Naval Research Logistics (NRL), John Wiley & Sons, vol. 66(8), pages 648-662, December.

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