Specifying a proper input distribution is often a challenging task in simulation modeling. In practice, there may be multiple plausible distributions that can fit the input data reasonably well, especially when the data volume is not large. In this …
We consider a Bayesian ranking and selection problem in the presence of input distribution uncertainty. The distribution uncertainty is treated from a robust perspective. A naive extension of the knowledge gradient (KG) policy fails to converge in …
Classical ranking-and-selection (R&S) procedures cannot be applied directly to select the best decision in the presence of distributional ambiguity. In this paper we propose a robust selection of the best (RSB) framework which compares decisions …