experiment; this design fixes the input combinations of the simulation model. These regression models uses a sequence of local …This chapter surveys two methods for the optimization of real-world systems that are modelled through simulation. These …-estimated through sequential designs. "Robust" optimization may use RSM or Kriging, and accounts for uncertainty in simulation inputs …

formulas, contrasting Kriging and classic linear regression metamodels. Furthermore, it extends Kriging to random simulation …

In practice, simulation analysts often change only one factor at a time, and use graphical analysis of the resulting … (DOE) and linear regression analysis. Unfortunately, classic theory assumes a single simulation response that is normally … and independently distributed with a constant variance; moreover, the regression (meta)model of the simulation model's I …

To analyze the input/output behavior of simulation models with multiple responses, we may apply either univariate or … the performance of univariate and multivariate Kriging, we perform several Monte Carlo experiments that simulate Gaussian …