A framework for Response Surface Methodology for simulation optimization
Proceedings of the 32nd conference on Winter simulation
Evolution and Optimum Seeking: The Sixth Generation
Evolution and Optimum Seeking: The Sixth Generation
Simulation Modeling and Analysis
Simulation Modeling and Analysis
Markovian models for performance and dependability evaluation
Lectures on formal methods and performance analysis
The Möbius Framework and Its Implementation
IEEE Transactions on Software Engineering
A Toolbox for Functional and Quantitative Analysis of DEDS
TOOLS '98 Proceedings of the 10th International Conference on Computer Performance Evaluation: Modelling Techniques and Tools
Numerical Analysis and Optimisation of Class Based Queueing
Proceedings of the 16th European Simulation Multiconference on Modelling and Simulation 2002
A Novel Approach for Fitting Probability Distributions to Real Trace Data with the EM Algorithm
DSN '05 Proceedings of the 2005 International Conference on Dependable Systems and Networks
Enhancing evolutionary algorithms with statistical selection procedures for simulation optimization
WSC '05 Proceedings of the 37th conference on Winter simulation
OPEDo: a tool for the optimization of performance and dependability models
ACM SIGMETRICS Performance Evaluation Review
Improving response surface methodology by using artificial neural network and simulated annealing
Expert Systems with Applications: An International Journal
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In general, decision support is one of the main purposes of model-based analysis of systems. Response surface methodology (RSM) is an optimization technique that has been applied frequently in practice, but few automated variants are currently available. In this paper, we show how to combine RSM with numerical analysis methods to optimize continuous time Markov chain models. Among the many known numerical solution methods for large Markov chains, we consider a Gauss-Seidel solver with relaxation that relies on a hierarchical Kronecker representation as implemented in the APNN Toolbox. To effectively apply RSM for optimizing numerical models, we propose three strategies which are shown to reduce the required number of iterations of the numerical solver. With a set of experiments, we evaluate the proposed strategies with a model of a production line and apply them to optimize a class-based queueing system.