基于随机森林算法的末端配送模式选择
首发时间:2018-08-02
摘要:本课题考虑在物流末端配送环节当中运用数据挖掘的技术手段,通过无监督学习模型判断末端配送模式的选择问题。本文首先对现有的末端配送模式进行分析,总结各种末端配送模式的特点。然后分析了随机森林算法的实现原理,通过调研等手段获得模型的训练集数据,结合机器学习随机森林的算法得到分析末端配送模式的模型。最后根据此模型对某个末端社区进行末端配送模式的选择。?
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Terminal Distribution Mode Selection Based on Random Forest Algorithm
Abstract:This paperconsiders the use of data mining techniques in the logistics end distribution, determining the choice of end delivery mode through unsupervised learning model.This paper first analyzes the existing end-delivery mode and summarizes the characteristics of various end-delivery modes.Then the realization principle of the random forest algorithm is analyzed. The training set data of the model is obtained through investigation and other means, and the model of the end distribution mode is obtained by combining the algorithm of machine learning random forest.Finally, according to this model, the end distribution mode is selected for an community.
Keywords: terminal-delivery;delivery mode;machine learning;random forest
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