具有动态客户的同时取送货车辆路径问题优化研究

发布时间：2018-04-23 09:27

本文选题：动态客户 + 同时取送货　；参考：《南京航空航天大学》2016年硕士论文

【摘要】：随着我国经济转型战略的不断深化与发展,企业对于自身的管理提出了更高的要求,特别是在占综合成本比例较高的物流管理方面。从企业角度出发,居高不下的物流成本以及效率低下的配送方式,大大削弱了企业的竞争力。因此,配送方式及运输成本作为影响物流成本和效率的主要因素,成为企业关注的重要问题以及研究的重点。一方面,从资源利用和节约能源的角度出发,回收利用的逆向物流得到了更多的关注,越来越多的企业需要实行同时进行取货和送货的配送方式,以此来避免浪费有效提高资源利用率。另一方面,配送过程中出现的各种不确定信息会对既有的配送计划产生极大影响,这也是企业需要解决的难题。移动通信技术、定位技术及智能设备的迅猛发展,为具有不确定信息的动态车辆路径问题提供了解决基础。因此,对符合现实情况的车辆路径问题进行研究,能够帮助企业降低成本,提高利润,增强竞争力。本文选取不确定信息中的一类,即动态客户问题,以更具效率的同时取送货车辆路径问题为依托展开研究,为企业解决车辆路径问题提供借鉴,论文进行的主要工作如下:首先,简明扼要的介绍了研究背景及意义,揭示了该研究的理论意义与现实价值,并对国内外针对该问题的研究做了总结与分析,阐明了论文的主要研究框架。其次,针对具有动态客户的同时取送货车辆路径问题进行了理论介绍,明确该问题的定义、主要分类,常见的求解算法及其优劣。文章选取蚁群算法作为解决问题的方法,并对该算法做了详细的介绍,为后续的方法提出提供了基础。第三,研究了不带时间窗的具有动态客户的同时取送货车辆路径问题,针对该问题的特点构建了数学模型。以蚁群优化算法为基础,结合实时插入方法,提出了符合问题特点的混合蚁群优化算法ACS-RIM算法,并通过A公司的实例对算法的可行性进行了验证,合理解决了带有动态客户并且没有时间窗要求的VRPSDP问题。第四,研究了带时间窗的具有动态客户的同时取送货车辆路径问题,结合问题具有时间窗要求的特殊性,重新构建合适的数学模型。另外,改进了ACS-RIM算法,从节省资源降低成本的角度出发,使算法的插入操作更能符合时间窗的要求,提高算法的求解速度和质量。通过对实际企业物流配送系统的优化,有效解决资源浪费的问题,达到降低运输成本,提高运输效率的目的。
[Abstract]:With the deepening and development of China's economic transformation strategy, enterprises have put forward higher requirements for their own management, especially in the aspect of logistics management, which accounts for a high proportion of comprehensive cost. From the point of view of enterprises, the high logistics cost and inefficient distribution greatly weaken the competitiveness of enterprises. Therefore, distribution mode and transportation cost, as the main factors affecting logistics cost and efficiency, have become an important issue and research focus of enterprises. On the one hand, from the point of view of resource utilization and energy conservation, the reverse logistics of recycling has received more attention. More and more enterprises need to carry out the delivery of goods and delivery at the same time. In order to avoid waste and improve the efficiency of resource utilization. On the other hand, all kinds of uncertain information in the distribution process will have a great impact on the existing distribution plan, which is also a difficult problem that enterprises need to solve. With the rapid development of mobile communication technology, positioning technology and intelligent equipment, the dynamic vehicle routing problem with uncertain information is solved. Therefore, the research on the vehicle routing problem in accordance with the actual situation can help enterprises to reduce costs, improve profits and enhance competitiveness. In this paper, we select a kind of uncertain information, that is, dynamic customer problem, based on the more efficient delivery vehicle routing problem, to provide a reference for enterprises to solve the vehicle routing problem. The main work of this paper is as follows: firstly, the research background and significance are briefly introduced, the theoretical significance and practical value of the research are revealed, and the domestic and foreign research on this problem is summarized and analyzed. The main research framework of this paper is expounded. Secondly, the paper introduces the problem of taking delivery vehicle routing with dynamic customers, defines the problem, mainly classifies it, and discusses the common algorithm and its advantages and disadvantages. In this paper, ant colony algorithm is selected as the method to solve the problem, and the algorithm is introduced in detail, which provides the foundation for the following methods. Thirdly, the routing problem of dynamic customers with delivery vehicles without time window is studied, and a mathematical model is built according to the characteristics of the problem. Based on the ant colony optimization algorithm and the real-time insertion method, a hybrid ant colony optimization algorithm, ACS-RIM algorithm, is proposed, and the feasibility of the algorithm is verified by the example of A company. Reasonable solution to the VRPSDP problem with dynamic customers and no time window requirements. Fourthly, the vehicle routing problem with dynamic customers with time windows is studied. According to the particularity of time windows, an appropriate mathematical model is constructed. In addition, the ACS-RIM algorithm is improved to save resources and reduce the cost, so that the insertion operation of the algorithm can meet the requirements of the time window, and improve the speed and quality of the algorithm. By optimizing the logistics distribution system of actual enterprises, the problem of waste of resources is effectively solved, and the purpose of reducing transportation cost and improving transportation efficiency is achieved.
【学位授予单位】：南京航空航天大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：U116.2

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