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Efficient Simulation Budget Allocation for Selecting an Optimal Subset

Department of Systems Engineering and Operations Research, George Mason University, Fairfax, Virginia 22030
Department of Systems Engineering and Operations Research, George Mason University, Fairfax, Virginia 22030
Robert H. Smith School of Business and Institute for Systems Research, University of Maryland, College Park, Maryland 20742
Department of Industrial and Systems Engineering, The National University of Singapore, Kent Ridge, 119260, Singapore
cchen9{at}gmu.edu
dhe1{at}gmu.edu
mfu{at}umd.edu
iseleelh{at}nus.edu.sg

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.

Key words: simulation optimization; computing budget allocation; ranking and selection
History: received August 2006; revised November 2007; accepted December 2007.