Simulation Modeling and Analysis
Simulation Modeling and Analysis
Computer experiments and global optimization
Computer experiments and global optimization
Orthogonal-column Latin hypercube designs with small samples
Computational Statistics & Data Analysis
A novel sequential design strategy for global surrogate modeling
Winter Simulation Conference
Comparison of experimental designs for simulation-based symbolic regression of manufacturing systems
Computers and Industrial Engineering
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Many simulation experiments require considerable computer time, so interpolation is needed for sensitivity analysis and optimization. The interpolating functions are 'metamodels' (or 'response surfaces') of the underlying simulation models. For sensitivity analysis and optimization, simulationists use different interpolation techniques (e.g. low-order polynomial regression or neural nets). This paper, however, focuses on Kriging interpolation. In the 1950's, D. G. Krige developed this technique for the mining industry. Currently, Kriging interpolation is frequently applied in Computer Aided Engineering. In discrete-event simulation, however, Kriging has just started. This paper discusses Kriging for sensitivity analysis in simulation, including methods to select an experimental design for Kriging interpolation.