An overview of the design and analysis of simulation experiments for sensitivity analysis

Sensitivity analysis may serve validation, optimization, and risk analysis of simulation models. This review surveys `classic' and `modern' designs for experiments with simulation models. Classic designs were developed for real, non-simulated systems in agriculture, engineering, etc. These designs assume `a few' factors (no more than 10 factors) with only `a few' values per factor (no more than five values). These designs are mostly incomplete factorials (e.g., fractionals). The resulting input/output (I/O) data are analyzed through polynomial metamodels, which are a type of linear regression models. Modern designs were developed for simulated systems in engineering, management science, etc. These designs allow `many factors (more than 100), each with either a few or `many' (more than 100) values. These designs include group screening, Latin hypercube sampling (LHS), and other `space filling' designs. Their I/O data are analyzed through second-order polynomials for group screening, and through Kriging models for LHS.