Metamodeling: a state of the art review
WSC '94 Proceedings of the 26th conference on Winter simulation
Bayesian analysis for simulation input and output
Proceedings of the 29th conference on Winter simulation
Proceedings of the 30th conference on Winter simulation
Response Surface Methodology: Process and Product in Optimization Using Designed Experiments
Response Surface Methodology: Process and Product in Optimization Using Designed Experiments
Simulation Modeling and Analysis
Simulation Modeling and Analysis
A sequential-design metamodeling strategy for simulation optimization
Computers and Operations Research
Bayesian methods for discrete event simulation
WSC '04 Proceedings of the 36th conference on Winter simulation
Sequential design and rational metamodelling
WSC '05 Proceedings of the 37th conference on Winter simulation
OPEDo: a tool framework for modeling and optimization of stochastic models
valuetools '06 Proceedings of the 1st international conference on Performance evaluation methodolgies and tools
Bayesian ideas and discrete event simulation: why, what and how
Proceedings of the 38th conference on Winter simulation
State-of-the-Art Review: A User's Guide to the Brave New World of Designing Simulation Experiments
INFORMS Journal on Computing
Kriging metamodel management in the design optimization of a CNG injection system
Mathematics and Computers in Simulation
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Protecting SLAs with surrogate models
Proceedings of the 2nd International Workshop on Principles of Engineering Service-Oriented Systems
Engineering autonomic controllers for virtualized web applications
ICWE'10 Proceedings of the 10th international conference on Web engineering
Surrogate modeling approximation using a mixture of experts based on EM joint estimation
Structural and Multidisciplinary Optimization
Sequential metamodelling with genetic programming and particle swarms
Winter Simulation Conference
Comparison of experimental designs for simulation-based symbolic regression of manufacturing systems
Computers and Industrial Engineering
Robust Optimization in Simulation: Taguchi and Krige Combined
INFORMS Journal on Computing
Accelerating universal Kriging interpolation algorithm using CUDA-enabled GPU
Computers & Geosciences
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Many simulation experiments require much computer time, so they necessitate interpolation for sensitivity analysis and optimization. The interpolating functions are 'metamodels' (or 'response surfaces') of the underlying simulation models. Classic methods combine low-order polynomial regression analysis with fractional factorial designs. Modern Kriging provides 'exact' interpolation, i.e., predicted output values at inputs already observed equal the simulated output values. Such interpolation is attractive in deterministic simulation, and is often applied in Computer Aided Engineering. In discrete-event simulation, however, Kriging has just started. Methodologically, a Kriging metamodel covers the whole experimental area; i.e., it is global (not local). Kriging often gives better global predictions than regression analysis. Technically, Kriging gives more weight to 'neighboring' observations. To estimate the Kriging metamodel, space filling designs are used; for example, Latin Hypercube Sampling (LHS). This paper also presents novel, customized (application driven) sequential designs based on cross-validation and bootstrapping.