Response surface methodology for constrained simulation optimization: An overview

Abstract

This article summarizes ‘generalized response surface methodology’ (GRSM), extending Box and Wilson’s ‘response surface methodology’ (RSM). GRSM allows multiple random responses, selecting one response as goal and the other responses as constrained variables. Both GRSM and RSM estimate local gradients to search for the optimum. These gradients are based on local first-order polynomial approximations. GRSM combines these gradients with Mathematical Programming findings to estimate a better search direction than the steepest ascent direction used by RSM. Moreover, these gradients are used in a bootstrap procedure for testing whether the estimated solution is indeed optimal. The focus of this paper is the optimization of simulated (not real) systems.