%0 Report
%9 IIASA Working Paper
%A Futschik, A.
%A Pflug, G.C.
%C IIASA, Laxenburg, Austria
%D 1996
%F iiasa:5002
%T Asymptotically Optimal Allocation of Simulation Experiments in Discrete Stochastic Optimization
%U https://pure.iiasa.ac.at/id/eprint/5002/
%X Approximate solutions for discrete stochastic optimization problems  are often obtained via simulation. It is reasonable to complement  these solutions by confidence regions for the argmin-set. We address the question, how a certain total number of random draws  should be distributed among the set of alternatives. We propose a one-step allocation rule which turns out to be  asymptotically optimal in the case of normal errors for two goals: To minimize the costs caused by using only an approximate solution  and to minimize the expected size of the confidence sets.