Proceedings Article10.1109/WSC.2000.899865
Multi-response simulation optimization using stochastic genetic search within a goal programming framework
Felipe F. Baesler,José Sepúlveda +1 more
- 01 Dec 2000
- Vol. 1, pp 788-794
TL;DR: The goal programming model integrated with the genetic algorithm and the stochastic search present a new approach able to lead a search towards a multi-objective solution.
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Abstract: This study presents a new approach to solve multi-response simulation optimization problems. This approach integrates a simulation model with a genetic algorithm heuristic and a goal programming model. The genetic algorithm technique offers a very flexible and reliable tool able to search for a solution within a global context. This method was modified to perform the search considering the mean and the variance of the responses. In this way, the search is performed stochastically, and not deterministically like most of the approaches reported in the literature. The goal programming model integrated with the genetic algorithm and the stochastic search present a new approach able to lead a search towards a multi-objective solution.
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References
•Book
Practical Genetic Algorithms
Randy L. Haupt,Sue Ellen Haupt +1 more
- 05 Jan 1998
TL;DR: Introduction to Optimization The Binary genetic Algorithm The Continuous Parameter Genetic Algorithm Applications An Added Level of Sophistication Advanced Applications Evolutionary Trends Appendix Glossary Index.
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Multiple response surface methods in computer simulation
TL;DR: This paper reviews the application of multiple re sponse surfaces to multiple-variable optimization problems and describes how these techniques may be used in analyzing computer simulation experiments.
95
A framework for simulation-optimization software
TL;DR: This paper reports on the work done to implement statistical error control within a heuristic search procedure, and on an automated procedure to deliver a statistical guarantee after the search procedure is finished.
84
Multicriteria design of manufacturing systems through simulation optimization
TL;DR: This paper describes an interactive (decision maker-computer) methodology for multiple response optimization of simulation models based on a multiple criteria optimization technique called the STEP method.
63
A Goal Programming Approach to the Optimization of Multi response Simulation Models
TL;DR: An approach within the framework of goal programming and uses a modified pattern search routine developed for this purpose is developed and the algorithm and a graphical example are presented.
62