A multi-category goods collecting and distributing vehicle path optimization method, device and equipment

By breaking down the chain store demand by product category, calculating the random transportation time matrix, and optimizing vehicle routes, the high cost problem in multi-category consolidation and delivery vehicle route planning in existing technologies is solved, achieving a more flexible and lower-cost logistics solution.

CN115907261BActive Publication Date: 2026-07-17CHANGAN UNIV
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
CHANGAN UNIV
Filing Date
2022-12-16
Publication Date
2026-07-17

AI Technical Summary

Technical Problem

Existing technologies fail to effectively consider the differences in cargo categories when planning vehicle routes for multi-category consolidation and delivery, resulting in high transportation costs, large time window penalty costs, and failure to effectively reduce carbon emissions and warehouse handling costs.

Method used

By breaking down the demand of chain stores into sub-nodes based on product categories, calculating the random transportation time matrix, and optimizing vehicle routes using an improved hybrid quantum particle swarm optimization neighborhood search algorithm, the optimal vehicle routes are planned by combining distance, time window, and quantity of goods.

Benefits of technology

It improves vehicle capacity utilization, reduces transportation and warehouse operation costs, reduces penalty costs for violating time windows, and provides a more effective logistics cost optimization solution.

✦ Generated by Eureka AI based on patent content.

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    Figure CN115907261B_ABST
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Abstract

The application discloses a multi-category goods collection and distribution vehicle path optimization method, device and equipment, a distance matrix of a chain store subnode is calculated according to the address of the chain store subnode, and a random transportation time matrix of the chain store subnode is calculated according to the distance matrix of the chain store subnode; the number of goods collection and distribution vehicles, the goods category that needs to be loaded when each goods collection and distribution vehicle departs from a warehouse, and the chain store subnode that needs to be visited and the visiting sequence of each goods collection and distribution vehicle are calculated according to the random transportation time matrix, the address, the goods taking and delivering time window, the goods to be delivered back, the quantity of the goods to be delivered back, the goods to be taken away and the quantity of the goods to be taken away of the chain store subnode. The application can plan a goods collection and distribution vehicle path more in line with actual demands, and reduce the operation cost of an enterprise.
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