TY  - RPRT
CY  - IIASA, Laxenburg, Austria
ID  - iiasa5002
UR  - https://pure.iiasa.ac.at/id/eprint/5002/
A1  - Futschik, A.
A1  - Pflug, G.C.
Y1  - 1996/03//
N2  - 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.
PB  - WP-96-023
M1  - working_paper
TI  - Asymptotically Optimal Allocation of Simulation Experiments in Discrete Stochastic Optimization
AV  - public
EP  - 18
ER  -