Study on auto parts suppliers composition selection based on adaptive genetic algorithm

2015 IEEE International Conference on Grey Systems and Intelligent Services (GSIS) Pub Date : 2015-10-26 DOI:10.1109/GSIS.2015.7301912

Shan Li, Zhao Yan, Lirong Jian, Jingwen Xu

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引用次数: 2

Abstract

In this paper, the adaptive genetic algorithm is applied to the auto parts suppliers selection problem, through the empirical analysis, verified by the feasibility and validity of the algorithm to solve such a problem. Firstly, this paper constructs a multi-objective mathematical model for suppliers composition selection, using the linear weighting method to converse this model to a single objective model. Secondly, this paper uses the adaptive genetic algorithm to solve the mathematical model, by dynamically adjusting the crossover mutation operator to accelerate the convergence speed of the algorithm. Finally, comparison and analysis of the contents of the two aspects are shown. 1: The result of the suppliers composition selection concluded by this paper and by K car company. 2: The performance of the adaptive genetic algorithm and standard genetic algorithm. Two points can be seen from the analysis results. 1: The genetic algorithm can be used to solve the auto parts suppliers composition selection problem. 2: By adjusting the crossover and mutation operator of the genetic algorithm dynamically, the inadequacy of the genetic algorithm can be improved upon.

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基于自适应遗传算法的汽车零部件供应商组成选择研究

本文将自适应遗传算法应用于汽车零部件供应商选择问题，通过实证分析，验证了该算法解决此类问题的可行性和有效性。首先，本文构建了供应商组合选择的多目标数学模型，利用线性加权法将该模型转化为单目标模型。其次，采用自适应遗传算法求解数学模型，通过动态调整交叉变异算子加快算法的收敛速度。最后，对两方面的内容进行了比较分析。1:本文与K汽车公司的供应商构成选择结果。2:自适应遗传算法和标准遗传算法的性能。从分析结果可以看出两点。1:遗传算法可用于解决汽车零部件供应商组成选择问题。2:通过动态调整遗传算法的交叉和变异算子，可以改进遗传算法的不足。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2015 IEEE International Conference on Grey Systems and Intelligent Services (GSIS)

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