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基于神经网络的集装箱码头泊位分配聚类分析
引用本文:严伟,杨露,黄有方,王煜.基于神经网络的集装箱码头泊位分配聚类分析[J].上海海事大学学报,2013,34(3):8-12.
作者姓名:严伟  杨露  黄有方  王煜
作者单位:上海海事大学 集装箱供应链技术教育部工程研究中心,上海海事大学 物流工程学院,上海海事大学 集装箱供应链技术教育部工程研究中心,上海海事大学 集装箱供应链技术教育部工程研究中心
基金项目:国家自然科学基金(71101090);交通运输部项目(2012 329 810 180);上海市教育委员会科研创新项目(12ZZ148,13YZ080);上海海事大学校基金(20120102,20110019)
摘    要:为充分利用港口既有的建设规模、提高经济效益,对集装箱码头的泊位分配进行研究.采用神经网络和聚类分析两种数据挖掘技术分析相关数据,得到相应的数据挖掘模型.先通过反向传播(Back Propagation,BP)神经网络分析各因素对泊位分配的影响程度,确定出主要因素;然后通过聚类分析中的两步聚类算法进行分析;最终制定集装箱码头泊位分配策略.该方法可为提高集装箱码头生产效率提供帮助.

关 键 词:集装箱码头    泊位分配    数据挖掘    神经网络    聚类分析
收稿时间:2013/1/18 0:00:00
修稿时间:4/9/2013 12:00:00 AM

The study of cluster analysis on berth allocation of container terminal based on neural network
Yan Wei,Yang Lu,Huang Youfang and Wang Yu.The study of cluster analysis on berth allocation of container terminal based on neural network[J].Journal of Shanghai Maritime University,2013,34(3):8-12.
Authors:Yan Wei  Yang Lu  Huang Youfang and Wang Yu
Institution:Container Supply Chain Technology Engineering Research Center of MOE, Shanghai Maritime University,Logistics Engineering College, Shanghai Maritime University
Abstract:To make full use of existing port scale and improve economic efficiency, the berth allocation in container terminals is studied. Two data mining methods, namely, neural network and cluster analysis, are employed to analyze related data and the corresponding data mining model is proposed. First, the Back Propagation (BP) neural network is used to analyze the effects of various factors on berth allocation to figure out main factors. Then, the two step clustering algorithm in cluster analysis is used to finally establish the berth allocation strategy. The proposed method can help to improve the productivity in container terminals.
Keywords:Cluster analysis on berth allocation of container terminal based on neural network
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