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广州港冷链物流系统优化与仿真 被引量:1

Optimization and Simulation of Cold Chain Logistics System in Guangzhou Port
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摘要 为了加快广州港冷链物流发展步伐,促进广州国际航运中心的建设,采用数学建模和FlexTerm仿真的方法对港口冷链物流堆存系统、集疏运系统进行优化。在冷藏箱混合堆存的基础上以各贝位间操作量的不平衡性最小、集卡行驶路径最短、场桥作业时间最短为目标函数分别构建贝位间作业量平衡分配模型、集卡路径优化模型和集装箱龙门吊调度优化模型。在K-means聚类分析的基础上,以集疏运总成本最小为目标函数构建珠三角和泛珠三角内陆集疏运线网优化模型。利用FlexTerm软件,以南沙三期集装箱码头的冷藏箱堆场为对象构建仿真模型,并将冷库涵盖在内。仿真输出结果验证堆存优化模型的有效性。采用Lingo和Matlab软件进行求解,结果表明各模型均能在一定程度上提高港口生产效率。 In order to speed up the development of cold chain logistics in Guangzhou Port and accelerate the construction of Guangzhou International Shipping Center, the methods of mathematical modeling and FlexTerm simulation are adopted to optimize the port cold chain logistics storage system and collecting and distributing system. On the basis of mixture storage of the refrigerated container, the job allocation model is constructed to balance the workloads between the different yard bays, the path optimization model is constructed to minimize the travelling distance of the port internal container trucks, and the scheduling optimization model is constructed to shorten the working hours of the container yard cranes. Based on the K-means clustering analysis, the optimization model of inland collecting and dispatching network in the Pearl River Delta region and pan-Pearl-River Delta region is established to minimize the total collecting and dispatching cost. The FlexTerm simulation software is used to simulate the handling and storage process of the refrigerated containers in Nansha phase III container terminal, which covers the refrigeration house. The simulation results show the effectiveness of the model. The Lingo and Matlab software is used to solve these optimization models, which indicates that these models can promote the port production efficiency in a certain extent.
出处 《港口科技》 2017年第12期1-9,43,共10页 Port Science & Technology
关键词 港口 广州港 集装箱码头 冷藏箱混堆优化 K-means聚类分析 FlexTerm 堆存策略 port Guangzhou Port container terminal refrigerated container mixture storage optimization K-means clustering analysis FlexTerm stacking strategy
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