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 … estimator uses parametric bootstrapping. Classic EI and bootstrapped EI are compared through various test functions, including …

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 …

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

This paper presents a novel heuristic for constrained optimization of random computer simulation models, in which one … of the simulation outputs is selected as the objective to be minimized while the other outputs need to satisfy … prespecified target values. Besides the simulation outputs, the simulation inputs must meet prespecified constraints including the …

In practice, most computers generate simulation outputs sequentially, so it is attractive to analyze these outputs … sequential estimator. We present a sequence of Monte Carlo experiments for quantifying the performance of these SPRTs. In … experiment #1 the simulation outputs are normal. This experiment suggests that Wald (1945)’s SPRT with estimated variance gives …

of the simulation responses, and introduce a new variant with the following two features: (1) this variant uses intrinsic …. 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 …

replications that varies with the input combination of the simulation model. To compare the performance of intrinsic Kriging and …Kriging provides metamodels for deterministic and random simulation models. Actually, there are several types of … estimation of the trend in the input-output data of the underlying simulation model; this estimation deteriorates the Kriging …

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 …

This paper uses a sequentialized experimental design to select simulation input combinations for global optimization …/output data of the simulation model (computer code). This paper adapts the classic "expected improvement" (EI) in "efficient … estimator uses parametric bootstrapping. Classic EI and bootstrapped EI are compared through four popular test functions …

, develop a `robust' methodology that accounts for uncertain environments. Our methodology uses Taguchi's view of the uncertain …