A large-scale customer point classification and distribution method based on meanshift classification
A kind of customer point, large-scale technology, applied in the direction of genetic model, special data processing application, instrument, etc., can solve the problem of not considering the road quality, the small number of network points of circulation capacity and so on
Active Publication Date: 2016-11-23
ENJOYOR COMPANY LIMITED +1
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[0006] In order to solve the problem of logistics and distribution of large-scale customer points, to overcome the shortcomings of existing distribution points that are calculated based on straight-line distances, without considering geographical information factors such as road quality and circulation capacity, and the number of outlets is small, etc., The invention provides a logistics distribution method that takes the actual road network distance between distribution points as the calculation basis, and simultaneously considers the actual driving capacity of the road, the large number of network points, and the time required by the distribution point for freight
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[0050] The present invention will be further described below in conjunction with the accompanying drawings.
[0051] refer to Figure 1 to Figure 5 , a large-scale customer classification distribution method based on meanshift classification, including the following steps:
[0052] A1. Obtain vector map (Shape files), which is a vector data format provided by ESRI, without topology information. A Shape files consists of a group of files, among which the necessary basic files include three files: coordinate file (.shp), index file (.shx) and property file (.dbf). Coordinate files (.shp) are used to record spatial coordinate information. The coordinate file is composed of header file and entity information. The index file (.shx) mainly contains the index information of the file, and each record in the file contains the offset of the corresponding coordinate file record from the file header of the coordinate file. The coordinate information of the specified target can be conv...
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A large-scale customer point classification and delivery method based on the meanshift algorithm, including the following steps: A1. Obtain road network vector data with 4 fields, process the three cases of under, over and node disjoint, and establish a GIS rich network Road network model, A2. Establish a distribution target node classification model; A3. Establish a vehicle optimal scheduling model; A4. First use the N-order shortest neighbor algorithm to determine the number k of large-scale customer point classifications. After the meanshift algorithm determines the large-scale customer point classification The cluster center of each cluster and the customer points contained in each cluster; A5. The delivery target nodes in each category are the original 1 / k, and then use the vehicle optimization scheduling algorithm to obtain the delivery results for the delivery target nodes in each category. The present invention takes the actual road network line distance between distribution points as the calculation basis, and simultaneously considers the actual driving capacity of the road, the large number of network points, and the time required by the distribution point for freight.
Description
technical field [0001] The invention relates to geographic information data processing, computer application fields, transportation engineering, management science and engineering, operations research, graph theory and network analysis, and especially relates to the field of logistics distribution. Background technique [0002] With the rapid development of economic globalization and network information technology, logistics distribution as a new economic growth point has attracted widespread attention. Distribution is the core link of the logistics system, and it is an inevitable market behavior born with the market. With the increasingly fierce market competition and the continuous improvement of customer requirements, distribution will play a pivotal role in the future market competition. [0003] Scholars at home and abroad are focusing on the VRPTW (vehicle routing problem with time windows) problem, mainly because it is the core problem of logistics distribution and tr...
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IPC IPC(8): G06F17/00G06Q10/08G06Q50/28G06N3/12
Inventor 张贵军陈铭明洁姚春龙张贝金程正华邓勇跃刘玉栋秦传庆
Owner ENJOYOR COMPANY LIMITED