Logistic distribution method based on cloud computing platform

Abstract

The invention discloses a logistic distribution method based on a cloud computing platform, which applies an ant colony optimization algorithm to logistic distribution path planning, realizes parallelization processing of the ant colony optimization algorithm based on the hadoop cloud computing platform, and realizes large-scale logistic distribution. According to the method, the ant colony optimization algorithm is realized by an HDFS (Hadoop Distributed File System) of the hadoop platform and a MapReduce parallel programming frame, the ant colony optimization algorithm is parallelized by MapReduce, each iteration time is accelerated, the HDFS is used for storing parameters and intermediate files, the parameter read and update rate of the ant colony optimization algorithm is increased due to the high efficiency and high reliability of the HDFS, and a path search progress is accelerated.

Description

technical field [0001] The invention relates to the field of logistics distribution, in particular to a method for logistics distribution based on a cloud computing platform. Background technique [0002] Logistics distribution is the link directly connected with consumers in logistics activities. Among the various costs of logistics, distribution costs account for a relatively high proportion. Whether the distribution route arrangement is reasonable or not has a great impact on the distribution speed, cost, and benefit. The determination of the distribution route is more complicated when the number of distribution users is large and there are many distribution nodes. It is an important part of logistics distribution to plan and arrange distribution routes in a scientific and reasonable way. The current route planning of logistics distribution mainly adopts conventional shortest path calculation methods, such as Dijkstra algorithm (Dijkstra), Floyd algorithm (Floyed), SPFA ...

AI Technical Summary

Problems solved by technology

The current route planning of logistics distribution mainly adopts conventional shortest path calculation methods, such as Dijkstra algorithm (Dijkstra), Floyd algorithm (Floyed), SPFA algorithm (Shortest Path Faster Algorithm), etc., the above algorithms are only used For applications with fewer path nodes and simple paths, when the number of path nodes increases and the path is complex, the calculation path speed of the algorithm is slow, or even unacceptable

With the rapid development of the logistics industry, the traditional logistics path calculation method can no longer meet the needs of the current large-scale logistics distribution and distribution. In view of this situation, the method of classification and group distribution is currently mainly used to reduce the number of path nodes. The main line and branch lines are planned to reduce the time of route calculation, but because the load on the main line and each logistics distribution center is overwhelmed, the operating cost is greatly increased, and the distribution of manpower and material resources on the branch lines cannot be dynamically coordinated, resulting in Great waste of human and material resources

[0003] In the prior art, CN102278996 discloses a method for path planning using the ant colony optimization algorithm, however, the ant colony optimization algorithm, as a swarm intelligence algorithm, has a slow convergence speed and is quite time-consuming

With the development of the scale of logistics distribution, more and more nodes are distributed, and the difficulty of path planning is becoming more and more difficult. The traditional optimization algorithm running on a single machine can no longer meet the current needs. Efficient and fast logistics distribution methods have become an urgent need. puzzle solved

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