网购环境下的两类车辆路径问题研究

发布时间：2018-03-10 10:09

本文选题：网上购物　切入点：车辆路径问题　出处：《重庆交通大学》2015年硕士论文　论文类型：学位论文

【摘要】：随着互联网科技和智能手机的快速普及,电子商务迎来了高速发展的良好时机,网上购物逐渐成为人们消费的重要手段。物流作为网上购物的线下实体交付的必要环节,在整个电子交易过程中扮演着至关重要的角色,优质的物流服务是电子商务企业盈利的重要保证,也是网购消费者的内心期待。通过高效、精准的线下物流配送交付,可以提高客户对于物流服务商的满意程度,从而增强了电子商务企业的客户忠诚度,有利于提高电商企业的市场占有率,带来更多的盈利。由于网上购物市场的竞争日益激烈,电子商务企业除了在价格上争取更多客户,纷纷将与之配套的物流配送服务视为急需提高的手段,多样化的物流配送服务应运而生。少数电商企业由于不满意良莠不齐的第三方物流服务商的服务质量,建立了自营物流配送体系,将大部分物流配送任务交由自己完成。在保证了自身网购物流服务水平的同时,还成为了多数物流企业模仿的标杆。在网购物流配送中,更多的电商企业将其物流业务委托给第三方物流服务商。而在配送途中,可能会产生多样的客户突发变动状况和退货要求,导致预先设计的车辆行驶路线很难得到实现,不恰当的处理方式则会造成严重的经济损失。本文根据目前网购物流配送的现实应用背景,对网购环境下的两类车辆路径问题展开研究。第一类,分析了有自营物流的电商企业的物流配送模式,对其自营物流成本与第三方物流成本分别展开研究,在此基础上建立了基于第三方的车辆路径模型,提出了带种群扩张机制的改进遗传算法对问题进行求解,数据试验结果表明了算法运算结果优良。第二类,研究了网购物流配送中的退货问题。利用动态管理方法对途中更改收货方式或取消收货问题进行分析,建立了以客户服务时间、车辆行驶路程和车辆行驶成本三部分总波动值之和最小化为优化目标的数学模型,利用嵌套分割方法求解模型,数据试验结果验证了算法求解效果良好。通过对临时拒收或有寄件需求问题的分析,将问题转化为同时集送货问题,建立同时集送货的车辆路径模型,考虑到该问题的多个约束,提出了设定极大数值染色体编码的遗传算法,并通过数据试验结果验证了模型的有效性和求解的可行性。
[Abstract]:With the rapid popularization of Internet technology and smart phones, e-commerce ushered in a good opportunity for rapid development. Online shopping has gradually become an important means for people to consume. Logistics is a necessary link in offline physical delivery of online shopping. In the entire electronic transaction process plays a vital role, the quality of logistics service is an important guarantee of the profit of e-commerce enterprises, and is also the heart of the consumers of online shopping expectations. Through efficient, accurate offline distribution delivery, It can improve the satisfaction of customers with logistics service providers, thus enhance the customer loyalty of e-commerce enterprises, help to increase the market share of e-commerce enterprises and bring more profits. Because of the increasingly fierce competition in the online shopping market, In addition to competing for more customers in price, e-commerce enterprises have regarded the matching logistics and distribution services as a means to be urgently improved. A small number of e-commerce enterprises are not satisfied with the service quality of the mixed third-party logistics service providers, so they have established their own logistics distribution system. It not only ensures the level of logistics service of its own online shopping, but also becomes the benchmark for most logistics enterprises to imitate. More e-commerce enterprises entrust their logistics business to third-party logistics service providers. On the way of distribution, they may produce a variety of customer sudden changes and return requirements, resulting in the difficulty of realizing pre-designed vehicle routes. Improper treatment will cause serious economic losses. According to the practical application background of online shopping logistics distribution, this paper studies two kinds of vehicle routing problems in online shopping environment. This paper analyzes the logistics distribution mode of e-commerce enterprises with self-owned logistics, studies the cost of self-operation logistics and the cost of third-party logistics, and establishes the vehicle routing model based on the third party. An improved genetic algorithm with population expansion mechanism is proposed to solve the problem. The experimental results show that the algorithm has good results. This paper studies the problem of returning goods in the logistics distribution of online shopping, analyzes the problem of changing the way of receiving goods or canceling receiving goods on the way by using dynamic management method, and establishes the time of customer service. The mathematical model, which minimizes the sum of the total fluctuating values of the three parts of vehicle travel distance and vehicle running cost, is used to solve the model by using nested segmentation method. The results of data test show that the algorithm has good results. By analyzing the problem of temporary rejection or the demand for sending goods, the problem is transformed into a simultaneous set delivery problem, and the vehicle routing model of simultaneous collection delivery is established. Considering the multiple constraints of the problem, a genetic algorithm with maximum numerical chromosome coding is proposed, and the validity of the model and the feasibility of its solution are verified by the data experiment results.
【学位授予单位】：重庆交通大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：TP301.6

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