%D 2012
%C Berlin, Germany
%A Gabriella Dellino
%A Carlo Meloni
%L eprints1444
%T Metamodel variability analysis combining bootstrapping and validation techniques
%I ACM
%X Research on metamodel-based optimization has received considerably increasing interest in recent years, and has found successful applications in solving computationally expensive problems. The joint use of computer simulation experiments and metamodels introduces a source of uncertainty that we refer to as metamodel variability. To analyze and quantify this variability, we apply bootstrapping to residuals derived as prediction errors computed from cross-validation. The proposed method can be used with different types of metamodels, especially when limited knowledge on parameters? distribution is available or when a limited computational budget is allowed. Our preliminary experiments based on the robust version of
the EOQ model show encouraging results.
%O Article No.: 334
%B WSC '12 Proceedings of the Winter Simulation Conference