Comparing systems via stochastic simulation: an enhanced two-stage selection procedure
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
On the Convergence of Pattern Search Algorithms
SIAM Journal on Optimization
Efficient Global Optimization of Expensive Black-Box Functions
Journal of Global Optimization
The ProC/B Toolset for the Modelling and Analysis of Process Chains
TOOLS '02 Proceedings of the 12th International Conference on Computer Performance Evaluation, Modelling Techniques and Tools
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Using Ranking and Selection to "Clean Up" after Simulation Optimization
Operations Research
DSN '05 Proceedings of the 2005 International Conference on Dependable Systems and Networks
Kriging interpolation in simulation: a survey
WSC '04 Proceedings of the 36th conference on Winter simulation
Simulation optimization: a review, new developments, and applications
WSC '05 Proceedings of the 37th conference on Winter simulation
Enhancing evolutionary algorithms with statistical selection procedures for simulation optimization
WSC '05 Proceedings of the 37th conference on Winter simulation
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Simulating process chain models with OMNeT++
Proceedings of the 1st international conference on Simulation tools and techniques for communications, networks and systems & workshops
| Hi-index | 0.00 |
A model-based design of systems requires appropriate tool support in many ways. It requires a modeling notation that suits the application problem, a set of analysis techniques that provide qualitative and/or quantitative results, and finally some optimization methods that help a designer to make appropriate design decisions. The challenge is to integrate those components into a homogenous framework such that a model based design takes advantage from synergy effects that result from a sophisticated combination of modeling formalism, analysis and optimization technique. In this paper, we present OPEDo, a tool framework that integrates modeling tools and analysis engines with state-of-the-art optimization methods. With respect to modeling, it contains the ProC/B editor for specifying open process-oriented simulation models, the APNN Toolbox for modeling with stochastic Petri nets, and OMNet++, for modeling using a simulation language. OPEDo provides analysis techniques for stochastic models based on discrete event simulation, based on queueing network analysis and numerical analysis techniques for continuous time Markov chains with the help of HIT, OMNeT++, and APNN Toolbox. Optimization of stochastic models has particular challenges due to the cost of model evaluation and the precision of results that can be achieved, so OPEDo contains specially adjusted variants of a variety of optimization methods, which includes response surface methodology, evolutionary strategies, genetic algorithms, and Kriging metamodeling techniques.