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基于神经网络的集装箱码头泊位分配聚类分析
  • ISSN号：1672-9498
  • 期刊名称：《上海海事大学学报》
  • 分类：U656.135[交通运输工程—港口、海岸及近海工程;交通运输工程—船舶与海洋工程] U691.31[交通运输工程—港口、海岸及近海工程;交通运输工程—船舶与海洋工程]
  • 作者机构：[1]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201306, [2]上海海事大学物流工程学院,上海201306
  • 相关基金：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）
作者： 严伟[1], 杨露[2], 黄有方[1], 王煜[1]
关键词： 集装箱码头, 泊位分配, 数据挖掘, 神经网络, 聚类分析, container terminal, berth allocation, data mining, neural network, cluster analysis
中文摘要：

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．

英文摘要：

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 es- tablish the berth allocation strategy. The proposed method can help to improve the productivity in contain- er terminals.

同期刊论文项目
高能效模式下的集装箱码头核心资源分配研究
期刊论文 50 会议论文 15
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期刊信息
  • 《上海海事大学学报》
  • 中国科技核心期刊
  • 主管单位:上海市教育委员会
  • 主办单位:上海海事大学
  • 主编：黄有方
  • 地址：上海浦东新区临港新城海港大道A30#
  • 邮编：201306
  • 邮箱：smucae@163.com
  • 电话：021-38284908
  • 国际标准刊号：ISSN：1672-9498
  • 国内统一刊号：ISSN：31-1968/U
  • 邮发代号:
  • 获奖情况:
  • CAJ-CD规范执行优秀期刊,中国期刊协会编校质量优秀期刊,全国高校编辑质量优秀科技期刊,上海市编校质量优秀期刊,上海市优秀学报,上海市审读优秀科技期刊,上海市新闻出版行业文明单位,中国高校科技期刊优秀团队
  • 国内外数据库收录:
  • 波兰哥白尼索引,美国剑桥科学文摘,中国中国科技核心期刊,中国北大核心期刊（2008版）,中国北大核心期刊（2011版）,中国北大核心期刊（2014版）
  • 被引量:2579