2017 ISE Graduate Student Research Award

Simulations are often driven by input models estimated from finite real-world data. When we use simulations to assess the performance of a stochastic system, there exist two sources of uncertainty in the performance estimates: input and simulation estimation uncertainty. In this paper, we develop a budget allocation approach that can efficiently employ the potentially tight simulation resource to construct a percentile confidence interval quantifying the impact of the input uncertainty on the system performance estimates, while controlling the simulation estimation error. Our approach is theoretically supported, and empirical studies also demonstrate it has superior and more robust performances than the direct bootstrapping.