Design of experiments for simulation models with stochastic constraints

Abstract

Design of experiments is often used to better understand or improve the output measures of a simulation model. Traditional cuboidal and spherical designs have been effective when the factor input regions are known. However, often in practice, simulation models have additional stochastic constraints causing the input factor space to be restricted and irregularly shaped. This paper looks into several alternative designs in a two stage approach to study these models in the constrained regions. This approach is then applied to a (s,S) inventory simulation. ©2009 IEEE.