Efficient Simulation Budget Allocation for Selecting an Optimal Subset

We consider a variation of the subset selection problem in ranking and selection, where motivated by recently developed global optimization approaches applied to simulation optimization, our objective is to identify the top-m out of k designs based on simulated output. Using the optimal computing budget 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 procedure that is easy to implement. Numerical experiments indicate that the resulting allocations are superior to other methods in the literature, and the relative efficiency increases for larger problems. 1 This work has been supported in part by the National Science Council of the Republic of China under Grant NSC 95-2811-E-002-009, by NSF under Grants IIS-0325074 and DMI-0323220, by NASA Ames Research Center under Grants NAG-2-1643 and NNA05CV26G, by FAA under Grant 00-G-016, and by AFOSR under Grant FA95500410210. 2 Corresponding author: Professor Chun-Hung Chen, Tel: 703-993-3572; Fax: 703-993-1521; Email: [email protected]; Web: mason.gmu.edu/~cchen9