Task package optimization method, system and device

An optimization method and wrapping technology, applied in the fields of instruments, character and pattern recognition, data processing applications, etc., can solve the problems of limited algorithm computing power, inability to find destructive formula, poor quality of solutions, etc., to achieve fast planning paths and simple structure. , the effect of reduced computing power requirements

Inactive Publication Date: 2017-11-24
SF TECH
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  • Application Information

AI Technical Summary

Problems solved by technology

[0003] In the algorithm in the field of route planning, it is usually to directly import massive package data into the algorithm model for route planning, but if the massive data is transferred into the algorithm model, that is, when the order of magnitude of the problem is too large, it means that the search space is huge and may can lead to very poor solution quality
It is also possible that due to the large input magnitude and the limited computing power of the algorithm, it is impossible to find a destructive problem that cannot find a feasible solution

Method used

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  • Task package optimization method, system and device
  • Task package optimization method, system and device
  • Task package optimization method, system and device

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Experimental program
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Embodiment 1

[0046] This embodiment provides a task package optimization method, including:

[0047] S1. Obtain the attribute information of multiple packages, the attribute information is address flow, packaging requirements and time window, where the address flow is the start and end address of the package, packaging requirements such as whether the package needs to be placed forward, and the time window is the customer Shipping time and requested delivery time;

[0048] S2. Establish a corresponding multi-dimensional vector according to the package attribute information, the multi-dimensional vector is address flow direction, packaging requirements and time window;

[0049] S3. Input the multi-dimensional vector into the k-means clustering algorithm to classify the parcels, wherein the parcels of the same classification group are input into the large-scale neighborhood search algorithm as a whole for path planning,

[0050] The specific steps are:

[0051] S31. Randomly select K vecto...

Embodiment 2

[0077] The features of this embodiment that are the same as those of Embodiment 1 will not be described in detail. The features of this embodiment that are different from Embodiment 1 are:

[0078] In the task package optimization method of this embodiment,

[0079] In step S1, the attribute information is the address flow direction, packaging requirements and time window, where the address flow direction is the start and end addresses of the package, the packaging requirements are such as pasting fragile instructions, and the time window is the customer’s mailing time and required delivery time;

[0080] Step S2: Establish a corresponding multi-dimensional vector according to the package attribute information, the multi-dimensional vector is address flow, packaging requirements and time window.

[0081] The selected number of cluster centers K=0.1×the total number of packages.

Embodiment 3

[0083] The features of this embodiment that are the same as those of Embodiment 1 will not be described in detail. The features of this embodiment that are different from Embodiment 1 are:

[0084] In the task package optimization method of this embodiment,

[0085] In step S1, the attribute information is the address flow, packing requirements and time window, wherein, the address flow is the starting and ending address of the package, the packing requirements such as commodity price labels are affixed to the outside of the package, and the time window is the delivery time and delivery time required by the customer. arrival time;

[0086] Step S2: Establish a corresponding multi-dimensional vector according to the package attribute information, the multi-dimensional vector is address flow, packaging requirements and time window.

[0087] The selected number of cluster centers K=0.8×the total number of packages.

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Abstract

The present invention relates to a task package optimization method, system and device. The method comprises: S1, obtaining attribute information of a plurality of packages; S2, establishing corresponding multi-dimensional vectors according to the package attribute information; and S3, performing classification of the packages through inputting the k-means clustering algorithm into the multi-dimensional vectors, wherein the packages with the same classification set are taken as a whole body to input the large-scale neighborhood search algorithm for path planning. order of magnitudes of the packages can be compressed to 10%-20% of the original so as to greatly reduce a search space and reduce the calculation capacity requirement for the anaphase algorithm, the packages processed through the k-means clustering algorithm satisfies the requirements of accurate, stable and rapid path planning of large-scale neighborhood search with no need for extra cost investment.

Description

technical field [0001] The present invention relates to path planning, in particular to a task package optimization method, system and equipment. Background technique [0002] With the rapid development of the logistics industry, the competition among the logistics industry is also intensifying. The shortening of logistics cost and cycle mainly focuses on the optimization of the path. Choosing the optimal path has become the most urgent need for logistics enterprises. Large-scale neighborhood search algorithm is one of the methods to solve such problems. [0003] In the algorithm in the field of route planning, it is usually to directly import massive package data into the algorithm model for route planning, but if the massive data is transferred into the algorithm model, that is, when the order of magnitude of the problem is too large, it means that the search space is huge and may can lead to very poor solution quality. It may also be due to the large input magnitude a...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06K9/62G06Q10/08
CPCG06Q10/047G06Q10/083G06F18/23213
Inventor 王宇高磊刘志欣杨志伟喻东武胡奉平孔晨
Owner SF TECH
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