This article uses a sequentialized experimental design to select simulation input combinations for global optimization …/output data of the simulation model (computer code). This design and analysis adapt the classic "expected improvement" (EI) in …

This paper studies simulation-based optimization with multiple outputs. It assumes that the simulation model has one …

In Longstaff and Schwartz (2001) a method for American option pricing using simulation and regression is suggested, and … since then the method has rapidly gained importance. However, the idea of using regression and simulation for American …

. As a novel metamodel we introduce intrinsic Kriging, for either deterministic or random simulation. For deterministic … simulation we study the famous 'e fficient 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 …

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 … first-order and second-order polynomials known as response surface methodology (RSM). Kriging models are global, but are re …

simulated system. EGO treats the simulation model as a black-box, and balances local and global searches. In deterministic … simulation, EGO uses ordinary Kriging (OK), which is a special case of universal Kriging (UK). In our EGO variant we use … intrinsic Kriging (IK), which eliminates the need to estimate the parameters that quantify the trend in UK. In random simulation …