Statistical tools for simulation practitioners
Statistical tools for simulation practitioners
Random number generation and quasi-Monte Carlo methods
Random number generation and quasi-Monte Carlo methods
Introduction to neural networks
Introduction to neural networks
The nature of statistical learning theory
The nature of statistical learning theory
Neural network design
Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 1
Computing confidence intervals for stochastic simulation using neural network metamodels
Computers and Industrial Engineering - Special issue on computational intelligence for industrial engineering
Information Processing and Management: an International Journal
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Past, present, and future of decision support technology
Decision Support Systems - Special issue: Decision support systems: Directions for the next decade
Efficient Global Optimization of Expensive Black-Box Functions
Journal of Global Optimization
Automatic Clustering of Software Systems Using a Genetic Algorithm
STEP '99 Proceedings of the Software Technology and Engineering Practice
Stochastic simulations of web search engines: RBF versus second-order regression models
Information Sciences—Informatics and Computer Science: An International Journal
A sequential-design metamodeling strategy for simulation optimization
Computers and Operations Research
Optimization and response surfaces: Gaussian radial basis functions for simulation metamodeling
Proceedings of the 34th conference on Winter simulation: exploring new frontiers
Neural Networks - 2005 Special issue: IJCNN 2005
Simulation optimization decision support system for ship panel shop operations
WSC '05 Proceedings of the 37th conference on Winter simulation
Model-driven decision support systems: Concepts and research directions
Decision Support Systems
Kriging metamodeling in constrained simulation optimization: an explorative study
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
A study of project selection and feature weighting for analogy based software cost estimation
Journal of Systems and Software
A comparative study of metamodeling methods for multiobjective crashworthiness optimization
Computers and Structures
Computers and Operations Research
Kriging metamodel with modified nugget-effect: The heteroscedastic variance case
Computers and Industrial Engineering
Hybrid models in agent-based environmental decision support
Applied Soft Computing
Bayesian Kriging Analysis and Design for Stochastic Simulations
ACM Transactions on Modeling and Computer Simulation (TOMACS)
A Bayesian metamodeling approach for stochastic simulations
Proceedings of the Winter Simulation Conference
Information Sciences: an International Journal
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Simulation is a widely applied tool to study and evaluate complex systems. Due to the stochastic and complex nature of real world systems, simulation models for these systems are often difficult to build and time consuming to run. Metamodels are mathematical approximations of simulation models, and have been frequently used to reduce the computational burden associated with running such simulation models. In this paper, we propose to incorporate metamodels into Decision Support Systems to improve its efficiency and enable larger and more complex models to be effectively analyzed with Decision Support Systems. To evaluate the different metamodel types, a systematic comparison is first conducted to analyze the strengths and weaknesses of five popular metamodeling techniques (Artificial Neural Network, Radial Basis Function, Support Vector Regression, Kriging, and Multivariate Adaptive Regression Splines) for stochastic simulation problems. The results show that Support Vector Regression achieves the best performance in terms of accuracy and robustness. We further propose a general optimization framework GA-META, which integrates metamodels into the Genetic Algorithm, to improve the efficiency and reliability of the decision making process. This approach is illustrated with a job shop design problem. The results indicate that GA-Support Vector Regression achieves the best solution among the metamodels.