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.