A methodology for fitting and validating metamodels in simulation

By Jack P. C. Kleijnen, R. Sargent. European Journal of Operational Research, 2000

543 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Industrial and Manufacturing Engineering, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/689cc0c2c917d5f225064c74bca78f6ddd837b56

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Jack P. C. Kleijnen
2. Rogelio Oliva
3. W. V. Beers
4. Bertrand Iooss
5. Amandine Marrel
6. O. Roustant
7. Béatrice Laurent
8. Alfons Balmann

In Industrial and Manufacturing Engineering

1. Rogelio Oliva
2. W. V. Beers
3. Bertrand Iooss
4. O. Roustant
5. Amandine Marrel
6. Béatrice Laurent
7. Jack P. C. Kleijnen

In Management Science and Operations Research

1. Jack P. C. Kleijnen
2. Rogelio Oliva
3. W. V. Beers
4. Bertrand Iooss
5. O. Roustant
6. Amandine Marrel
7. Béatrice Laurent
8. Alfons Balmann

Citations in the corpus

• Investigations into the effect of tool shapes with size factor consideration in sink electrical discharge machining (EDM) process (2009, background): “These polynomial regression models can help engineers to make system design decisions [24, 25].”
• Identification and Review of Sensitivity Analysis Methods (2002, unknown): “Furthermore, sensitivity analysis can play an important role in model verification and validation throughout the course of model development and refinement (e.g., Kleijnen, 1995; Kleijnen and Sargent, 2000; Fraedrich and Goldberg, 2000).”
• A Review on Global Sensitivity Analysis Methods (2014, unknown): “) on a test basis, via cross-validation or bootstrap (Kleijnen and Sargent [46], Fang et al.”
• Calculations of Sobol indices for the Gaussian process metamodel (2008, methodology): “These methods have been generalized to develop surrogates for costly computer codes (Sacks et al. [23], Kleijnen & Sargent [13]).”
• Sample size for binary logistic prediction models: Beyond events per variable criteria (2018, methodology): “The simulation results are summarized using metamodels.(27,28) This paper is structured as follows.”
• Model calibration as a testing strategy for system dynamics models (2003, methodology): “There is a range of options for defining the error function f and the relative weight of each error series wi (Kleijnen and Sargent, 2000; see also Reichelt et al., 1996 for a detailed comparison of error functions and their relative attributes when used to estimate cyclical SD models).”
• Application-driven sequential designs for simulation experiments: Kriging metamodelling (2004, methodology): “To validate the design, Sacks et al. (1989, p. 416) Note that criteria such as (4) are more appropriate in sensitivity analysis than in simulation optimization; see Sasena et al. (2002) and also Kleijnen and Sargent (2000) and Kleijnen (1998).”
• Research, part of a Special Feature on Empirical based agent-based modeling Agent-based Analysis of Agricultural Policies: an Illustration of the Agricultural Policy Simulator AgriPoliS, its Adaptation and Behavior (2006, result): “It uses the fitted meta-model to predict the outcomes of new parameter combinations and to compare these predictions with the corresponding simulation responses (Kleijnen and Sargent 2000).”