一种改进的L-OD法及其应用
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摘要
在物流需求预测中常常存在着地域差异大、基础数据收集难的问题,本文在理论研究和实践的基础上提出一种改进的L-OD法。该方法在将预测地区划分为内部小区和外部影响区的基础上,提出结合主成分分析和聚类分析的区域物流水平分类方法,应用弹性系数法预测物流总量,根据发生吸引权重分析内部小区的物流生成量,构建综合权重模型计算影响区的物流生成量,构建双约束重力模型预测物流分布量。最后应用改进的L-OD预测四川省物流需求,预测结果与四川省的发展规划契合,并且能够为未来四川省"十四.五"相关规划提供科学的数据支撑。
In the forecast of logistics demand, there are often problems of large regional differences and difficult data collection. This paper proposes an improved L-OD method based on theoretical research and practice. Based on the classification of the predicted area into the internal area and the external influence area,this method proposes a regional logistics level classification method based on principal component analysis and cluster analysis. The elastic coefficient method is used to predict the total amount of logistics, and the internal community is analyzed according to the occurrence of the attraction weight. The amount of logistics generated, the comprehensive weight model is constructed to calculate the amount of logistics generated in the affected area, and the double-constrained gravity model is constructed to predict the distribution of logistics.Finally, the improved L-OD is used to predict the logistics demand in Sichuan Province. The forecast results are consistent with the development plan of Sichuan Province, and can provide scientific data support for the future "14 th Five-Year Plan" in Sichuan Province.
In the forecast of logistics demand, there are often problems of large regional differences and difficult data collection. This paper proposes an improved L-OD method based on theoretical research and practice. Based on the classification of the predicted area into the internal area and the external influence area,this method proposes a regional logistics level classification method based on principal component analysis and cluster analysis. The elastic coefficient method is used to predict the total amount of logistics, and the internal community is analyzed according to the occurrence of the attraction weight. The amount of logistics generated, the comprehensive weight model is constructed to calculate the amount of logistics generated in the affected area, and the double-constrained gravity model is constructed to predict the distribution of logistics.Finally, the improved L-OD is used to predict the logistics demand in Sichuan Province. The forecast results are consistent with the development plan of Sichuan Province, and can provide scientific data support for the future "14 th Five-Year Plan" in Sichuan Province.
引文
[1]杨荣英,张辉,苗张木.物流预测技术中的移动平均线方法[J].武汉理工大学学报(交通科学与工程版),2001(03):353-355.
[2]吴德会.动态指数平滑预测方法及其应用[J].系统管理学报,2008(02):151-155.
[3]杨蕾,张苗苗.时间序列模型在物流需求预测中的应用[J].商业时代,2013(13):26-27.
[4]王正新.灰色多变量GM(1,N)幂模型及其应用[J].系统工程理论与实践,2014,34(09):2357-2363.
[5]曾胜.中国能源消费、经济增长与能源需求预测的研究[J].管理评论,2011,23(02):38-44.
[6]秦俭.基于粗糙集与多元回归的我国物流需求预测[J].物流技术,2014,33(13):298-299+311.
[7]孙焰,赵剑翔,郑文家.基于多种群遗传BP的城市物流需求预测[J].综合运输,2016,38(03):126-132.
[8]韩瑞.基于集对分析的区域物流需求预测研究[D].保定:河北大学,2018.
[9]张锦. L-OD预测理论与现代物流规划方法研究[D].成都:西南交通大学,2004.
[10]陈导.基于L-OD的物流需求预测系统的研究与实现[D].成都:西南交通大学,2008.
[14]田丽娜.基于L-OD法的地区间物流需求预测分析——以四川省泸州市为例[J].商场现代化,2011(08):66-67.
[12]易美.基于L-OD的城市地下物流网络规划方法研究[D].长沙:长沙理工大学,2016.
[13]马丽.基于聚类分析的山东省十七城市物流发展水平研究[D].济南:山东大学,2016.
[14]李晓萌,姜丽敏.山西省物流发展水平综合评价——基于主成分分析[J].时代金融,2017(03):52.
[15]戴海龙.山东省地级城市物流发展水平评价研究[D].淄博:山东理工大学,2018.
[2]吴德会.动态指数平滑预测方法及其应用[J].系统管理学报,2008(02):151-155.
[3]杨蕾,张苗苗.时间序列模型在物流需求预测中的应用[J].商业时代,2013(13):26-27.
[4]王正新.灰色多变量GM(1,N)幂模型及其应用[J].系统工程理论与实践,2014,34(09):2357-2363.
[5]曾胜.中国能源消费、经济增长与能源需求预测的研究[J].管理评论,2011,23(02):38-44.
[6]秦俭.基于粗糙集与多元回归的我国物流需求预测[J].物流技术,2014,33(13):298-299+311.
[7]孙焰,赵剑翔,郑文家.基于多种群遗传BP的城市物流需求预测[J].综合运输,2016,38(03):126-132.
[8]韩瑞.基于集对分析的区域物流需求预测研究[D].保定:河北大学,2018.
[9]张锦. L-OD预测理论与现代物流规划方法研究[D].成都:西南交通大学,2004.
[10]陈导.基于L-OD的物流需求预测系统的研究与实现[D].成都:西南交通大学,2008.
[14]田丽娜.基于L-OD法的地区间物流需求预测分析——以四川省泸州市为例[J].商场现代化,2011(08):66-67.
[12]易美.基于L-OD的城市地下物流网络规划方法研究[D].长沙:长沙理工大学,2016.
[13]马丽.基于聚类分析的山东省十七城市物流发展水平研究[D].济南:山东大学,2016.
[14]李晓萌,姜丽敏.山西省物流发展水平综合评价——基于主成分分析[J].时代金融,2017(03):52.
[15]戴海龙.山东省地级城市物流发展水平评价研究[D].淄博:山东理工大学,2018.