An experimental procedure for simulation response surface model identification

TLDR

The paper presents an experimental method to identify suitable simulation response surface models. The method uses frequency‑domain experiments with oscillating inputs to determine influential factors and the functional form of a response surface, measuring term significance and matching correlation‑based ranking under specified conditions. Frequency‑domain experiments reduce the number of required simulation runs compared to conventional approaches.

Abstract

An experimental method for identifying an appropriate model for a simulation response surface is presented. This technique can be used for globally identifying those factors in a simulation that have a significant influence on the output. The experiments are run in the frequency domain. A simulation model is run with input factors that oscillate at different frequencies during a run. The functional form of a response surface model for the simulation is indicated by the frequency spectrum of the output process. The statistical significance of each term in a prospective response surface model can be measured. Conditions are given for which the frequency domain approach is equivalent to ranking terms in a response surface model by their correlation with the output. Frequency domain simulation experiments typically will require many fewer computer runs than conventional run-oriented simulation experiments.