Simulation Solution Screening Using Functional Properties
Simulation models today give rise to problems with large numbers of simulated scenarios or solutionsmore than can be simulated exhaustively. Fortunately, users of these models may be able to verify or infer properties, such as convexity, of a performance measure of interest when viewed as a function over the space of solutions. In Plausible Screening Using Functional Properties for Simulations with Large Solution Spaces, Eckman, Plumlee and Nelson introduce a framework in which such properties are exploited to avoid simulating solutions with unacceptable performances. Their methods solve optimization problems that measure how well the result of a limited simulation experiment agrees with the claim that a solution is acceptable. These methods deliver desirable statistical guarantees of confidence and consistency. Numerical experiments illustrate how functional properties coupled with small simulation experiments can avoid many simulations for simulation-optimization problems.