Genetic algorithm-based redundancy qptimization problems in fuzzy framework

Hou Fujun; Wu Qizong

doi:10.1080/03610920600728716

Genetic algorithm-based redundancy qptimization problems in fuzzy framework

Hou Fujun^*
, Wu Qizong

^*此作品的通讯作者

Beijing Institute of Technology

科研成果: 期刊稿件 › 文章 › 同行评审

3 引用（Scopus）

摘要

This article uses a genetic algorithm to solve the series parallel redundancy optimization problem which is in a fuzzy framework. Three nonlinear chance constrained programing models and three goal programing models are formulated based on possibility measure and credibility measure. A fuzzy simulation-based genetic algorithm is then employed to solve these kinds of fuzzy programing. Finally, numerical examples are also given.

源语言	英语
页（从-至）	1931-1941
页数	11
期刊	Communications in Statistics - Theory and Methods
卷	35
期	10
DOI	https://doi.org/10.1080/03610920600728716
出版状态	已出版 - 1 10月 2006

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10.1080/03610920600728716

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abstract = "This article uses a genetic algorithm to solve the series parallel redundancy optimization problem which is in a fuzzy framework. Three nonlinear chance constrained programing models and three goal programing models are formulated based on possibility measure and credibility measure. A fuzzy simulation-based genetic algorithm is then employed to solve these kinds of fuzzy programing. Finally, numerical examples are also given.",

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N2 - This article uses a genetic algorithm to solve the series parallel redundancy optimization problem which is in a fuzzy framework. Three nonlinear chance constrained programing models and three goal programing models are formulated based on possibility measure and credibility measure. A fuzzy simulation-based genetic algorithm is then employed to solve these kinds of fuzzy programing. Finally, numerical examples are also given.

AB - This article uses a genetic algorithm to solve the series parallel redundancy optimization problem which is in a fuzzy framework. Three nonlinear chance constrained programing models and three goal programing models are formulated based on possibility measure and credibility measure. A fuzzy simulation-based genetic algorithm is then employed to solve these kinds of fuzzy programing. Finally, numerical examples are also given.

KW - Credibility measure

KW - Fuzzy simulation

KW - Genetic algorithm

KW - Possibility measure

KW - Redundancy optimization

UR - https://www.scopus.com/pages/publications/33750522795

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DO - 10.1080/03610920600728716

M3 - Article

AN - SCOPUS:33750522795

SN - 0361-0926

VL - 35

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JO - Communications in Statistics - Theory and Methods

JF - Communications in Statistics - Theory and Methods

IS - 10

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Genetic algorithm-based redundancy qptimization problems in fuzzy framework

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