Introduction -- Classic regression metamodels and their designs -- Classic assumptions versus simulation practice … -- Screening the many inputs of realistic simulation models -- Kriging metamodels and their designs -- Simulation optimization. … Simulation Experiments (DASE). Altogether, this new edition has approximately 50% new material not in the original book. More …

. 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 …

To analyze the input/output behavior of simulation models with multiple responses, we may apply either univariate or …

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 …

in a realistic simulation experiment. A specific method is sequential bifurcation (SB), which is a sequential method that …) second-order polynomials are adequate approximations of the input/output functions implied by the simulation model; (ii) the … important second-order effects either (heredity property). This paper examines SB for random simulation with multiple responses …