Efficient simulation budget allocation for selecting an optimal subset

  • Chen C
  • He D
  • Fu M
 et al. 
  • 27

    Readers

    Mendeley users who have this article in their library.
  • 105

    Citations

    Citations of this article.

Abstract

We consider a class of the subset selection problem in ranking and selection. The objective is to identify the top m out of k designs based on simulated output. Traditional procedures are conservative and inefficient. Using the optimal computing budget allocation framework, we formulate the problem as that of maximizing the probability of correctly selecting all of the top-m designs subject to a constraint on the total number of samples available. For an approximation of this correct selection probability, we derive an asymptotically optimal allocation and propose an easy-to-implement heuristic sequential allocation procedure. Numerical experiments indicate that the resulting allocations are superior to other methods in the literature that we tested, and the relative efficiency increases for larger problems. In addition, preliminary numerical results indicate that the proposed new procedure has the potential to enhance computational efficiency for simulation optimization.

Author-supplied keywords

  • Computing budget allocation
  • Ranking nd
  • Selection
  • Simulation optimization

Get free article suggestions today

Mendeley saves you time finding and organizing research

Sign up here
Already have an account ?Sign in

Find this document

Authors

  • Chun Hung Chen

  • Donghai He

  • Michael Fu

  • Loo Hay Lee

Cite this document

Choose a citation style from the tabs below

Save time finding and organizing research with Mendeley

Sign up for free