应用PSO算法的港口集疏运系统模型优化研究--以大连港为例  

Research on Optimization Model for Port Collecting- Distributing Transportation System Based on PSO Algorithm——Take Dalian Port for Example

作　　者：李桂芝[1] 

机构地区：[1]营口理工学院,辽宁营口115000

出　　处：《物流工程与管理》2016年第5期122-123,共2页Logistics Engineering and Management

基　　金：辽宁省自然科学基金资助项目(L2013540)

摘　　要：由于港口集疏运系统指标众多、相互间相关性较强、定性与定量指标交叉等原因,一直难以建立有效的系统优化模型。文中对港口集疏运系统的影响指标进行分析,采用SPSS20.0和Matlab软件技术,通过对选取指标进行系统聚类分析、指标提取,并将定性与定量指标相结合等手段,以大连港集疏运系统为例建立集疏运系统的回归模型,并提出集疏运综合指数来衡量集疏运综合水平,使用微粒群优化算法(PSO)进行优化。结果表明,该方法在剔除多重共线性指标、有效融合定性指标等方面效果较好,同时通过构建集疏运系统优化模型,为港口相关政策提供必要的理论支撑。It has been difficult to establish an effective system optimization model because of too many port collecting-distributing transportation system indicators,a strong correlation with each other,qualitative and quantitative crossing.In this paper,impact indicators port transport system is analyzed using SPSS20.0 and Matlab software technology,and collection and distribution transportation system regression model taking Dalian port for example is set up by selecting indicators for Hierarchical Clustering analysis,extraction of indicators,and the combination of qualitative and quantitative indicators and other means.The paper proposes collecting and distributing transportation composite index to measure the overall level of collection and distribution transportation,using particle swarm optimization （PSO ） to optimize.The results show that the method in removing multicollinearity indicators,integrating effectively the qualitative indicators is better,and provides the necessary theoretical support for port-related policies by constructing an optimization model.

关 键 词：集疏运 优化模型 微粒群 综合指数 

分 类 号：F550[经济管理—产业经济]