Asymptotically Optimal Allocation of Simulation Experiments in Discrete Stochastic Optimization

Futschik, A. & Pflug, G.C. ORCID: https://orcid.org/0000-0001-8215-3550 (1996). Asymptotically Optimal Allocation of Simulation Experiments in Discrete Stochastic Optimization. IIASA Working Paper. IIASA, Laxenburg, Austria: WP-96-023

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Abstract

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.

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