@techreport{iiasa5002,
           month = {March},
            type = {IIASA Working Paper},
           title = {Asymptotically Optimal Allocation of Simulation Experiments in Discrete Stochastic Optimization},
         address = {IIASA, Laxenburg, Austria},
       publisher = {WP-96-023},
            year = {1996},
             url = {https://pure.iiasa.ac.at/id/eprint/5002/},
        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.},
          author = {Futschik, A. and Pflug, G. C.}
}