A logistics transportation route intelligent planning system and method

By redistributing routes based on node delivery association entropy and gravity under the crowdsourced delivery model, the problem of uneven resource allocation in traditional logistics transportation route planning is solved, achieving balanced resource allocation and efficient completion of delivery tasks.

CN120410382BActive Publication Date: 2025-11-14GUIZHOU BUSINESS SCHOOL
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Patent Information

Application Number
CN202510898318.4
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2025-07-01
Publication Date
2025-11-14
Estimated Expiration
2045-07-01

AI Technical Summary

Technical Problem

Traditional logistics route planning algorithms are ill-suited to the complexities of dynamic changes in the number of personnel, uneven distribution of delivery nodes, and large differences in delivery success rates under the crowdsourced delivery model. This results in uneven resource allocation, with some areas experiencing excessive delivery pressure while other areas have idle resources.

Method used

By obtaining the logistics transportation route map of the target area, extracting transportation route nodes, constructing a minimum objective function model, redistributing routes based on node delivery rate, cluster density, and gravity, optimizing route planning using the ant colony algorithm, and redistributing trajectories by combining node delivery association entropy and gravity, a balanced allocation of resources is achieved.

Benefits of technology

It improves the balance of route resource allocation in the crowdsourced delivery model, avoids resource waste, ensures that delivery tasks have sufficient manpower support, and achieves a balance of delivery in both space and time.

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Abstract

This application provides an intelligent logistics transportation route planning system and method. It obtains an initial logistics transportation route by planning transportation routes within a target area; determines the node clustering density corresponding to each transportation route node based on the neighborhood association characteristics of the nodes; then determines the node delivery association entropy of the target area based on the node clustering density and node delivery rate of each transportation route node; determines the number of crowdsourced deliveries and the node delivery attraction corresponding to each transportation route node based on the node delivery association entropy; and performs trajectory reallocation of the initial logistics transportation route based on the number of crowdsourced deliveries and the node delivery attraction of each transportation route node to obtain an intelligent logistics transportation trajectory. This method enables the reallocation of delivery routes based on the node delivery association entropy and node delivery attraction of the target area, improving the balance of route resource allocation under the crowdsourced delivery model.
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Citation Information

Patent Citations

  • A method for predicting related important indexes of newly-built logistics nodes in a freight transport network

    CN109002934A

  • Logistics vehicle distribution planning method and system based on distribution entropy multi-target particle swarm

    CN112488386A