Please use this identifier to cite or link to this item: https://doi.org/10.1287/ijoc.l080.0268
Title: Efficient simulation budget allocation for selecting an optimal subset
Authors: Chen, C.-H.
He, D.
Fu, M.
Lee, L.H. 
Keywords: Computing budget allocation
Ranking nd
Selection
Simulation optimization
Issue Date: Sep-2008
Source: Chen, C.-H.,He, D.,Fu, M.,Lee, L.H. (2008-09). Efficient simulation budget allocation for selecting an optimal subset. INFORMS Journal on Computing 20 (4) : 579-595. ScholarBank@NUS Repository. https://doi.org/10.1287/ijoc.l080.0268
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. © 2008 INFORMS.
Source Title: INFORMS Journal on Computing
URI: http://scholarbank.nus.edu.sg/handle/10635/63115
ISSN: 10919856
DOI: 10.1287/ijoc.l080.0268
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