基于遗传神经网络的汽车回收逆向物流综合评价

Comprehensive evaluation of vehicle recovery of reverse logistics based on genetic neural network

目前面向汽车回收的逆向物流工作实施效果的好坏难以衡量,针对这一问题,构建了汽车回收逆向物流综合评价指标体系。采用可以获得专家知识经验的层次分析法计算指标体系的权重作为神经网络的输入,实现了定性分析和定量分析的有效结合;提出了用遗传算法优化神经网络的遗传神经网络评价方法,结合实例验证了该评价方法更加稳定迅速并具有时效性。实验结果表明提出的评价方法可以为汽车制造企业的逆向物流管理提供决策依据。

Currently it is difficult to measure implementation effect of reverse logistics work facing the vehicle recovery.To overcome this problem,made up a comprehensive evaluation index system for vehicle recovery of reverse logistics.Used analytic hierarchy process which could obtain expert knowledge and experience to calculate the weight of index system as neural network＇s input,which realized effective combination of qualitative analysis and quantitative analysis.Then,proposed an evaluation methodology using genetic algorithm to optimize neural network.That was verified that it was more stable,quickly and effective by combining with an example.Experimental results indicate that the proposed evaluation method can provide decision-making to reverse logistics management for automobile manufacturing enterprises.