Simulation optimization: methods and applications
Proceedings of the 29th conference on Winter simulation
Modeling at the machine-control level using discrete event simulation (DES)
Proceedings of the 30th conference on Winter simulation
Multicriteria optimization of simulation models
WSC '91 Proceedings of the 23rd conference on Winter simulation
Simulation optimization methodologies
Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 1
Design of experiments: robust design: seeking the best of all possible worlds
Proceedings of the 32nd conference on Winter simulation
Simulation optimization: a survey of simulation optimization techniques and procedures
Proceedings of the 32nd conference on Winter simulation
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Simulation Modeling and Analysis
Simulation Modeling and Analysis
Genetic Algorithms
A Mathematical Theory of Communication
A Mathematical Theory of Communication
Computers and Electronics in Agriculture
A Lexicographic Nelder-Mead simulation optimization method to solve multi-criteria problems
Computers and Industrial Engineering
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This paper describes a methodology for solving Parameter Design (PD) problems in production and business systems of considerable complexity. The solution is aimed at determining optimum settings to system critical parameters so that each system response is at its optimum performance level with least amount of variability. When approaching such problem, analysts are often faced with four major challenges: representing the complex parameter design problem, utilizing an effective search method that is able to explore the problem's complex and large domain, making optimization decisions based on multiple and, often, conflicting objectives, and handling the stochastic variability of in system response as an integral part of the search method. to tackle such challenges, this paper proposes a solution methodology that integrates four state-of-the-art modules of proven methods: Simulation Modeling (SM), Genetic Algorithm (GA), Entropy Method (EM), and Robustness Module (RM).