. As a novel metamodel we introduce intrinsic Kriging, for either deterministic or random simulation. For deterministic … simulation we study the famous `efficient global optimization' (EGO) method, substituting intrinsic Kriging for universal Kriging …. For random simulation we investigate a state-of-the-art two-stage algorithm accounting for heteroscedastic variances of …

This contribution presents an overview of sensitivity analysis of simulation models, including the estimation of … also reviews factor screening for simulation models with very many factors, focusing on the so-called "sequential … aim at the optimization of the simulated system, allowing multiple random simulation outputs. …

This article uses a sequentialized experimental design to select simulation input com- binations for global … input/output data of the simulation model (computer code). This design and analysis adapt the clas- sic "expected …

We take a fresh look at Theil's BLUS residuals and ask why they have gone out of fashion.All our simulation experiments …

used for modeling average or steady-state behavior of complex stochastic systems.Recently, simulation-based methods have …

a realistic simulation experiment. Sequential bifurcation (SB) is a sequential method that changes groups of inputs …-order polynomials are adequate approximations of the input/output functions implied by the simulation model; (ii) the signs of all first … either (heredity property). This paper examines SB for random simulation with multiple responses (outputs), called …

engineering case-studies that may raise ethical questions; these case studies employ simulation models. Next, I present an …

simulation models. This contribution discusses the different types of DOE for these three domains, but focusses on random … simulation. DOE may have two goals: sensitivity analysis including factor screening and optimization. This contribution starts …