M. Isabel Reis dos Santos

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The construction of a nonlinear regression metamodel for simulation requires experimental designs that better explore the nonlinearities of the system. The proposed sequential procedure focuses on simulation scenarios in sub-regions where the input–output behavior is more interesting. It takes into account, not only the inputs, but also the output(More)
Metamodels are used as analysis tools for solving optimization problems or as surrogates used as building blocks in larger scale simulations. The metamodel replaces the simulation model by a simplified input-output relationship, frequently a mathematical function with customized parameters. The construction of a metamodel is based on the simulation results(More)
11 Abstract 12 Linear regression metamodels have been widely used to explain the behavior of computer simulation models, 13 although they do not always provide a good global fit to smooth response functions of arbitrary shape. In the case study 14 discussed in this paper, the use of several linear regression polynomial results in a poor fit. The use of a(More)
In this paper, we investigate and discuss some of the main issues concerning the estimation of nonlinear simulation metamodels. We propose a methodology for identifying a tentative functional relationship, estimating the metamodel coefficients and validating the simulation metamodel. This approach is illustrated with a simple queueing system. Finally , we(More)
This article explores the use of metamodels as simulation building blocks. The metamodel replaces a part of the simulation model with a mathematical function that mimics the input–output behavior of that part, with respect to some measure of interest to the designer. The integration of metamodels as components of the simulation model simplifies the model(More)
Frequently, the main objective of statistically designed simulation experiments is to estimate and validate regression metamodels, where the regressors are functions of the design variables and the dependent variable is the system response. In this article, a weighted least squares procedure for estimating the unknown parameters of a nonlinear regression(More)