Logistics delivery route optimizing generation method and system

A route and logistics technology, applied in logistics, instruments, data processing applications, etc., can solve the problems of complex roads, difficult to promote, and high requirements for technical support capabilities

Inactive Publication Date: 2014-10-15
四川省烟草公司广安市公司 +1
View PDF3 Cites 20 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

According to the extraction of the characteristics of these cases, the domestic dynamic delivery mode can be divided into two schools: "dynamic delivery mode" and "flexible delivery mode". Dynamic delivery; the disadvantages are: high technical support capability requirements, large investment, only a few benchmarking enterprises can develop and apply, and it is difficult to promote in the industry status quo
The advantages of the flexible delivery mode are: simple and easy to understand; the disadvantages are: it is mainly based on manual combination optimization, and it has high requirements for the ability of the line arranger. After the logistics environment changes, it is difficult to adjust the delivery line. The common problems of the two modes are Difficult to promote
[0004] The delivery route optimization problem is a typical shortest path problem. However, due to cigarette terminal distribution, it has the characteristics of multi-faceted customer points, sales fluctuations, and complex roads, so the shortest path problem model is not flexible enough to solve the actual problem of terminal distribution in the tobacco industry.
Moreover, the amount of basic data collected for tobacco logistics and distribution is large, and the traditional method generally adopts a point-to-point calculation method, so the information collection and modeling are relatively complicated, and the calculation is difficult
At the same time, because there are a large number of terminal branches in the tobacco distribution network, when the algorithm calculates the last distribution route, the remaining customers are basically in each terminal branch, and the routes are extremely circuitous

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Logistics delivery route optimizing generation method and system
  • Logistics delivery route optimizing generation method and system
  • Logistics delivery route optimizing generation method and system

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0082] figure 1 The flow chart of Example 1 of the method for generating an optimized logistics delivery route provided by the embodiment of the present invention is shown in the figure: a method for generating an optimized logistics delivery route provided by the present invention includes the following steps:

[0083] S1: Obtain customer information in the preset delivery area with the logistics center as the origin V0;

[0084] S2: Aggregate customer information according to preset constraints to form a distribution area;

[0085] S3: Measure the working time between each area and the logistics center and the working time between adjacent areas;

[0086] S4: Determine the customer's linear delivery sequence number and the customer's delivery volume in the same area;

[0087] S5: Obtain the distribution volume, delivery vehicle volume and total delivery volume of each area;

[0088] S6: Select the area farthest away from the distribution working hours of the logistics cen...

Embodiment 2

[0147] image 3 The flow chart of Embodiment 2 of the logistics delivery route optimization generation method provided by the embodiment of the present invention, as shown in the figure, the only difference between this embodiment and Embodiment 1 is:

[0148] S21: Acquiring customer delivery point information in the delivery area;

[0149] S22: Aggregate customer distribution point information according to preset constraints to form a distribution area;

[0150] S23: Measure the distance from each area to the logistics center and the area;

[0151] S24: Obtain the unsatisfied orders of customers in each area and in the area;

[0152] S25: Judging whether each area is in the optimized route list and whether the delivery volume of each area is completed;

[0153] S26: If yes, proceed to step S; if no, traverse each area to determine whether the required delivery volume is less than the carrying capacity of large vehicles;

[0154] S27: If not, judge whether to choose to sen...

Embodiment 3

[0163] Figure 4 The working process diagram of Embodiment 3 of the logistics delivery route optimization generation method provided by the embodiment of the present invention, as shown in the figure, the difference between this embodiment and Embodiment 1 is only:

[0164] In the figure, A is the distribution center, that is, the calculation source point; each point in the figure constitutes the area that needs to be reached during the distribution process; the value between points is determined by time, and can also be understood as between points On the basis of the length of the path, add the time constraints of each point (such as: parking time, unloading time, etc.), and then convert it into a working time condition.

[0165]The following example specifically illustrates the working process of the logistics delivery route optimization generation method provided in this embodiment:

[0166] a) Let the logistics center be the origin V0, W is the set of customer areas Vm (...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses a logistics delivery route optimizing generation method and system. The logistics delivery route optimizing generation method comprises the following steps: firstly, obtaining and combining client information in distribution areas to form distribution districts; then, selecting the district which takes the longest distribution working time from a logistics center as a calculation source point according to delivery car capacity; accumulating client delivery capacity according to client linear distribution serial numbers in the district or a next delivery district which takes the shortest working time from the source point until the delivery car capacity is satisfied; and finally, finishing a total delivery amount, and outputting the selected client linear distribution serial numbers as an optimal delivery route. A proper delivery path is calculated by adopting a reverse recursion Dijkstra algorithm, the district is used as a basic calculation unit, a distribution network is simplified, a network calculation amount is greatly lowered, information acquisition difficulties and information acquisition time are greatly lowered and shortened respectively, the amount and the dispatch frequency of the delivery cars are reduced, operation cost is lowered, and distribution efficiency is improved.

Description

technical field [0001] The invention relates to the field of logistics delivery route planning, in particular to a method and system for generating an optimized logistics delivery route in the process of tobacco delivery. Background technique [0002] Regarding the exploration of delivery modes and route optimization, domestic tobacco logistics mostly adopt the method of "combining lines and reducing staff" to reduce the logistics cost of the "four fixed" mode. With the passage of time, this method has approached the limit. Restricted by these factors, there is no way to reduce it; "combining lines to reduce people" reaches a certain level, and it also conflicts with the concept of "people-oriented logistics". Adopting a new delivery mode to reduce logistics costs and improve operational efficiency has become a new topic of tobacco logistics research. [0003] Domestically, the exploration of the dynamic delivery mode has been carried out and good application results have b...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q10/08G06Q50/28
Inventor 何海晏刘丹云李永强杜兴华杨旭唐明华廖勇张勤金东平魏远辉蒋星新邓锐王诗瑶王欢何大志马荣辉朱祥军
Owner 四川省烟草公司广安市公司
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap