Sequential sampling for Bayesian robust ranking and selection

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 the new robust setting. We propose several stationary policies that extend KG in various aspects. Numerical experiments show that the proposed policies have excellent performance in terms of both probability of correction selection and normalized opportunity cost

Simulation metamodeling in the presence of model inadequacy

A simulation model is often used as a proxy for the real system of interest in a decision-making process. However, no simulation model is totally representative of the reality. The impact of the model inadequacy on the prediction of system performance should be carefully assessed. We propose a new metamodeling approach to simultaneously characterize both the simulation model and its model inadequacy. Our approach utilizes both simulation outputs and real data to predict system performance, and accounts for four types of uncertainty that arise from the unknown performance measure of the simulation model, simulation errors, unknown model inadequacy, and observation errors of the real system, respectively. Numerical results show that the new approach provides more accurate predictions in general