基于双满意度的电商自营物流末端配送VRP模型及应用

发布时间：2018-05-06 13:16

本文选题：双满意度 + 电商　；参考：《北京交通大学》2017年硕士论文

【摘要】：近年来,中国网络零售市场的交易规模呈现出爆发式的增长态势。面对日益严峻的物流配送问题,国内各大B2C电商平台纷纷推出了自己的物流配送解决方案,并形成了两种主要的电商物流配送模式。第一种是以天猫为代表的第三方物流配送模式,另一种是以京东商城为代表的自营物流配送体系的物流配送模式。随着B2C电商自营物流规模的不断扩大,B2C电商自营物流体系不仅可以满足电商平台中自营商品的需求,而且可以为入驻平台的部分卖家提供物流服务。自营电商物流末端配送站要为平台消费者(买家)、自营商城和部分入驻平台的卖家提送配送或者取货服务。物流配送客户满意度是衡量配送质量的重要因素之一,在传统的电商物流末端配送站进行取配送任务的设计时,大多数配送站没有将客户满意度考虑在内,部分考虑客户满意度的配送站也忽视了买家和卖家对物流配送需求不同的特点,将买家或卖家笼统的考虑为客户点进行统一的满意度评价。本文提出的基于买家和卖家双满意度的电商物流末端配送VRP模型根据买家和卖家对物流需求的性质不同将客户满意度分为买家满意度和卖家满意度,在双满意度的基础上对配送站的取配任务进行设计,更符合企业的实际情况,对企业管理者针对不同客户群体制定配送方案有一定的借鉴意义。本文首先详细介绍了 B2C自营物流电商企业的背景和在末端配送中所遇到的问题,并对国内外关于电商物流、客户满意度、VRP等研究成果和理论基础进行详细的分析和总结。其次,本文着重分析了自营物流电商的管理现状,进一步明确了本文的研究对象和研究问题,并针对问题提出了基于双满意度的末端配送VRP优化方案,同时,本文针对买家和卖家对物流配送需求不同的情况,结合国内外学者对客户满意度的研究成果,分别构建出了买家和卖家的满意度函数。然后,本文将买家和卖家的满意度函数转化为惩罚函数,并以车辆运输成本、车辆启动成本、惩罚成本最小化为优化目标,构建了电商物流末端配送VRP模型。最后,本文将J企业的实例数据带入到模型中,引入遗传算法进行求解,并与传统的VRP方案进行比较,验证了优化方案的有效性。
[Abstract]:In recent years, the transaction scale of Chinese network retail market presents explosive growth trend. In the face of increasingly severe logistics distribution problems, each domestic B2C e-commerce platform has launched its own logistics distribution solutions, and formed two main e-commerce logistics distribution mode. The first is the third party logistics distribution model represented by Tmall, and the other is the logistics distribution mode of the self-owned logistics distribution system represented by JingDong Mall. With the continuous expansion of the scale of B2C e-commerce self-operation logistics system, B2C e-commerce self-run logistics system can not only meet the demand of self-run goods in the e-commerce platform, but also provide logistics services for some sellers who have entered the platform. E-commerce logistics terminal distribution station for platform consumers (buyers, self-owned shopping mall and some of the sellers in the platform delivery or delivery services. Customer satisfaction of logistics distribution is one of the important factors to measure the quality of distribution. In the traditional design of the terminal distribution station of e-commerce logistics, most distribution stations do not take customer satisfaction into account. Some of the distribution stations considering customer satisfaction also ignore the different characteristics of buyers and sellers on the demand for logistics distribution and generally consider buyers or sellers for customer satisfaction evaluation. The VRP model of terminal distribution of e-commerce logistics based on buyer's and seller's satisfaction is divided into buyer's satisfaction and seller's satisfaction according to the nature of buyer's and seller's demand for logistics. On the basis of double satisfaction, the design of the assignment of distribution station is more in line with the actual situation of the enterprise, and it has certain reference significance for the enterprise managers to formulate the distribution plan for different customer groups. This paper firstly introduces the background of B2C self-run logistics e-commerce enterprise and the problems encountered in terminal distribution, and makes a detailed analysis and summary of domestic and foreign research results and theoretical basis on e-commerce logistics, customer satisfaction and VRP. Secondly, this paper focuses on the analysis of the current management situation of self-run logistics e-commerce, further defines the research object and research problems, and puts forward a dual-satisfaction based terminal distribution VRP optimization scheme, at the same time, According to the different demand of buyer and seller for logistics distribution, combined with the research results of domestic and foreign scholars on customer satisfaction, this paper constructs the satisfaction function of buyer and seller respectively. Then, the satisfaction function of buyer and seller is transformed into penalty function, and the VRP model of terminal distribution of e-commerce logistics is constructed with the optimization goal of vehicle transportation cost, vehicle start-up cost and penalty cost minimization. Finally, the example data of J enterprise is introduced into the model, and the genetic algorithm is introduced to solve the problem, and compared with the traditional VRP scheme, the effectiveness of the optimization scheme is verified.
【学位授予单位】：北京交通大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：F724.6;F259.2

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