低温奶网点配送中的车辆路径问题研究

发布时间：2018-05-18 14:33

本文选题：低温奶 + 车辆路径问题　；参考：《浙江理工大学》2017年硕士论文

【摘要】：随着消费者对新鲜、健康产品的偏好日益增加,国内消费者在奶制品上对低温奶的需求量也随之增加。因低温奶保质期短、易腐易损,从生产到销售需要全程冷链作支撑,冷链物流特别是冷链物流配送在低温奶的流通中发挥着巨大的作用。近年来,我国冷链物流得到迅猛发展,城市中单个物流配送中心需要服务的网点越来越多,由此构成的配送网络错综复杂,为低温奶网点配送带来了挑战。此外,由于低温奶对时效性要求高,配送半径有限,如何合理安排车辆的行驶路线,快速安全的进行低温奶配送以缩短产品的在途时间,提高配送效率具有重要的现实意义。因此,本文以城市低温奶网点配送为研究对象,采用先分组后路线的两阶段法研究低温奶网点配送中的车辆路径问题。本文主要工作和贡献如下:(1)低温奶网点分区研究。定义了最短干道距离,以实际距离代替欧式距离作为网点分区依据。同时考虑距离和密度对k-medoids聚类算法初始中心进行优化,增加配送量约束,对网点进行分区聚类。(2)低温奶网点配送中车辆路径问题模型构建与求解。根据低温奶网点配送的特点,构建了低温奶网点配送最短路径模型。提出了一种基于模拟退火的离散型布谷鸟搜索算法(SA-DCS),并使用算例与改进的布谷鸟搜索算法(RKCS)进行仿真对比,以验证算法的有效性。(3)应用案例研究。选用Y食品有限公司真鲜奶吧配送真实数据,开展低温奶网点配送中的车辆路径问题案例研究,进一步验证模型和算法的有效性。研究表明,本文所提出的改进的k-medoids聚类算法具有良好的有效性和鲁棒性,考虑配送量约束的聚类质量明显优于未考虑配送量约束的聚类结果。且本文提出的基于模拟退火的离散型布谷鸟搜索算法在寻优精度与搜索性能上具有良好的表现。此外,由上述两者构成的先分组后路线的两阶段法被证明是求解车辆路径问题的有效方法。
[Abstract]:With consumers' preference for fresh and healthy products increasing, domestic consumers' demand for low temperature milk also increases. Cold chain logistics, especially cold chain logistics, play a great role in the circulation of low temperature milk because of its short shelf life and perishable and vulnerable. In recent years, the cold chain logistics in our country has been developing rapidly, and the single logistics distribution center in the city needs more and more outlets to serve. The complicated distribution network has brought challenges to the low-temperature milk distribution network. In addition, due to the high requirement of low temperature milk on timeliness and the limited distribution radius, it is of great practical significance to arrange the driving route of the vehicle reasonably, to quickly and safely distribute the low temperature milk in order to shorten the time on the way of the product and to improve the distribution efficiency. Therefore, this paper takes the urban low-temperature milk distribution as the research object, uses the two-stage method of grouping first and then the route to study the vehicle routing problem in the distribution of low-temperature milk network. The main work and contribution of this paper are as follows: 1. The shortest trunk road distance is defined and the actual distance is used to replace the Euclidean distance as the basis of dot partition. At the same time, considering the distance and density, the initial center of the k-medoids clustering algorithm is optimized, and the distribution volume constraints are increased. According to the characteristics of low temperature milk distribution, the shortest path model of low temperature milk distribution was constructed. A discrete-time cuckoo search algorithm based on simulated annealing is proposed, and a numerical example is compared with the improved Cuckoo search algorithm (RKCS) to verify the effectiveness of the algorithm. In order to verify the validity of the model and the algorithm, a case study of the vehicle routing problem in the distribution of low-temperature milk outlets was carried out by using the true milk bar of Y Food Co., Ltd. The research shows that the improved k-medoids clustering algorithm proposed in this paper has good effectiveness and robustness, and the clustering quality considering distribution constraints is obviously superior to the clustering results without considering the distribution volume constraints. The proposed discrete cuckoo search algorithm based on simulated annealing has good performance in searching accuracy and performance. In addition, the two-stage method, which consists of the above two methods, has been proved to be an effective method for solving the vehicle routing problem.
【学位授予单位】：浙江理工大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：F252.1;TP18

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