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 …

underlying simulation models; such metamodels serve sensitivity analysis and optimization, especially for computationally … expensive simulations. In practice, simulation analysts often know that the I/O function is monotonic. To obtain a Kriging … metamodel that preserves this known shape, this article uses bootstrapping (or resampling). Parametric bootstrapping assuming …

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 …

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 …

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 …

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

This article reviews so-called screening in simulation; i.e., it examines the search for the really important factors … in experiments with simulation models that have very many factors (or inputs).The article focuses on a most e¢ cient and …