Quality & Quantity

, Volume 44, Issue 2, pp 315–332 | Cite as

Robust design in multivariate systems using genetic algorithms

Article

Abstract

This paper presents a methodology based on genetic algorithms, which finds feasible and reasonably adequate solutions to problems of robust design in multivariate systems. We use a genetic algorithm to determine the appropriate control factor levels for simultaneously optimizing all of the responses of the system, considering the noise factors which affect it. The algorithm is guided by a desirability function which works with only one fitness function although the system may have many responses. We validated the methodology using data obtained from a real system and also from a process simulator, considering univariate and multivariate systems. In all cases, the methodology delivered feasible solutions, which accomplished the goals of robust design: obtain responses very close to the target values of each of them, and with minimum variability. Regarding the adjustment of the mean of each response to the target value, the algorithm performed very well. However, only in some of the multivariate cases, the algorithm was able to significantly reduce the variability of the responses.

Keywords

Robust design Taguchi methods Genetic algorithms Desirability functions 

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Copyright information

© Springer Science+Business Media B.V. 2008

Authors and Affiliations

  • Hector Allende
    • 1
    • 2
  • Daniela Bravo
    • 1
  • Enrique Canessa
    • 1
  1. 1.Facultad de Ciencia y TecnologíaUniversidad Adolfo IbáñezVina del MarChile
  2. 2.Departamento de InformáticaUniversidad Técnica Federico Santa MaríaValparaisoChile

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