Importance Sampling in Optimization under Uncertainty using Surrogate Models

For the purpose of computing the expected value of a stochastic optimization problem via simulation, we propose a method for efficiently constructing importance sampling distributions using surrogate modeling. A software implementation of the methods called SMAIS is available on github. We use this software in experiments to demonstrate that our method can outperform Monte Carlo simulation. We also show good parallel efficiency for up to 16 processors allowing a speed up of more than 10. Our method uses adaptive sample sizes so it is not very sensitive to sample size parameters.