区域快递业务量预测及接驳点选址问题研究

发布时间：2018-05-07 23:32

本文选题：快递业务量 + 预测　；参考：《浙江工商大学》2015年硕士论文

【摘要】：快递业是我国的新兴行业,发展迅速,2010年至2013年我国快递业务量从23.4亿件上升到92亿件,年增长率高达40%；2014年全年,我国快递服务企业业务量累计完成139.6亿件,跃居世界第一,业务收入累计完成2045.4亿元,同比增长41.9%。尽管快递业发展速度迅猛,但其发展仍然存在诸多问题,例如：经营成本高、经济效益较低、服务质量不高、顾客满意度低等。尤其在快递业务量快速增长的背景下,这势必会影响快递业长远发展。为此,本文从宏观的快递业务量预测和微观的客户点分析这两个方面来分析如何提高服务质量和顾客满意度。本文首先构建了基于影响因素的快递业务量关系模型和基于时间序列的区域快递业务量组合预测模型,对区域快递业务量进行预测。根据基于影响因素的快递业务量关系模型得出市辖区2005-2014年的快递业务量,再根据基于时间序列的区域快递业务量组合预测模型得出区域未来三年的快递业务量；其次,根据区域的快递量预测结果,明确该区在未来具体需要的配送车辆数以及每辆车的具体接驳点位置。针对配送车辆数问题,先通过调研获取1辆车每年的配送快递量,然后根据区域快递预测量,求出配送所需的车辆数。针对每辆车的具体接驳点地址问题,构建选址模型对快递接驳点进行选址。模型包括三部分：第一,利用改进的K-means算法对客户点地址数据进行聚类分析；第二,对聚类得到的每类数据利用重心法求得初选接驳点位置；第三,通过频度分析以及停车难易度分析确定最优接驳点位置。最后,通过实证分析进行验证：第一,利用所建模型得出杭州市上城区未来三年的快递业务量；第二,利用得出的快递业务量数据以及某快递公司提供的其在杭州市某区域某时间周期内的真实数据,通过接驳点选址模型确定该区域最优的接驳点位置。
[Abstract]:Express delivery industry is a new industry in China, which has developed rapidly. From 2010 to 2013, the volume of express delivery business in our country rose from 2.34 billion to 9.2 billion, with an annual growth rate of 40%. In 2014, the total business volume of express delivery service enterprises in China reached 13.96 billion pieces, ranking first in the world. Total business income completed 204.54 billion yuan, an increase of 41.9%. Despite the rapid development of express industry, there are still many problems in its development, such as high operating cost, low economic benefit, low service quality, low customer satisfaction and so on. Especially in the context of rapid growth of express delivery business, this will inevitably affect the long-term development of the express industry. Therefore, this paper analyzes how to improve the quality of service and customer satisfaction from two aspects of macro express delivery volume prediction and micro customer point analysis. In this paper, first of all, the relationship model of express service volume based on influence factors and the combined forecasting model of regional express business volume based on time series are constructed to predict the regional express business volume. According to the relationship model of express delivery volume based on influencing factors, the express business volume from 2005 to 2014 is obtained, and then the regional express business volume in the next three years is obtained according to the forecasting model of regional express business volume based on time series. Secondly, According to the forecast result of regional express quantity, the number of distribution vehicles needed in the future and the location of the specific connection point of each vehicle in the area are determined. In order to solve the problem of the number of distribution vehicles, we first obtain the annual delivery quantity of one vehicle through investigation and research, and then calculate the number of vehicles needed for distribution according to the regional express forecast. To solve the problem of the specific connection point address of each vehicle, the location model is constructed to locate the express connection point. The model consists of three parts: first, the improved K-means algorithm is used to cluster the customer point address data; second, the center of gravity method is used to obtain the location of the primary connection point; third, Through frequency analysis and parking difficulty analysis to determine the optimal location of the connection point. Finally, through the empirical analysis to verify: first, using the established model to get the next three years of Hangzhou Shangcheng express business volume; second, By using the data of express delivery volume and the real data provided by a express delivery company in a certain period of time in a certain area of Hangzhou, the optimal location of the connection point in this area is determined by the location model of the connection point.
【学位授予单位】：浙江工商大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：F259.2

【参考文献】