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Discrete Dynamics in Nature and Society
Volume 2014 (2014), Article ID 195054, 9 pages
Research Article

Efficient Simulation Budget Allocation for Ranking the Top Designs

1School of Statistics, Southwestern University of Finance and Economics, Chengdu 611130, China
2Department of Industrial and Systems Engineering, National University of Singapore, Singapore 117576

Received 28 February 2014; Revised 22 May 2014; Accepted 4 June 2014; Published 18 June 2014

Academic Editor: Rigoberto Medina

Copyright © 2014 Hui Xiao and Loo Hay Lee. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


We consider the problem of ranking the top designs out of alternatives. Using the optimal computing budget allocation framework, we formulate this problem as that of maximizing the probability of correctly ranking the top designs subject to the constraint of a fixed limited simulation budget. We derive the convergence rate of the false ranking probability based on the large deviation theory. The asymptotically optimal allocation rule is obtained by maximizing this convergence rate function. To implement the simulation budget allocation rule, we suggest a heuristic sequential algorithm. Numerical experiments are conducted to compare the effectiveness of the proposed simulation budget allocation rule. The numerical results indicate that the proposed asymptotically optimal allocation rule performs the best comparing with other allocation rules.