Bootstrapping and validation of metamodels in simulation
Proceedings of the 30th conference on Winter simulation
Robust simulation-optimization using metamodels
Winter Simulation Conference
Global sensitivity analysis of stochastic computer models with joint metamodels
Statistics and Computing
Robust Optimization in Simulation: Taguchi and Krige Combined
INFORMS Journal on Computing
A framework for input uncertainty analysis
Proceedings of the Winter Simulation Conference
Simultaneous kriging-based estimation and optimization of mean response
Journal of Global Optimization
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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.