Intelligent culture gene logistics distribution method based on similarity learning

A cultural gene and logistics distribution technology, applied in logistics, instruments, data processing applications, etc., can solve the problems of not considering the size of the fleet, slow algorithm calculation speed, easy to fall into local optimum, etc., to avoid premature convergence and reduce manpower Material cost, effect of improving search capability

Active Publication Date: 2018-11-30
GUANGDONG UNIV OF TECH
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AI Technical Summary

Problems solved by technology

This method uses the genetic algorithm to calculate by inputting the penalty information of delivery failure, which satisfies the time constraint to a certain extent and saves the distance cost in the delivery process, but its algorithm has the disadvantages of slow calculation speed and easy to fall into local optimum. Taking into account practical factors such as fleet size and road congestion

Method used

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  • Intelligent culture gene logistics distribution method based on similarity learning
  • Intelligent culture gene logistics distribution method based on similarity learning
  • Intelligent culture gene logistics distribution method based on similarity learning

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Embodiment

[0055] figure 1 It is a flow chart of an intelligent cultural gene logistics distribution method based on similarity learning, and an intelligent cultural gene logistics distribution method based on similarity learning includes the following steps:

[0056] S1: Upload cargo data, customer coordinates, road status and other data to the database, and determine the scheduling model of delivery vehicles with a limited time limit;

[0057] S2: population initialization;

[0058] S3: Check whether the stop condition is met, if the stop condition is met, the chromosome is decoded to obtain the optimal distribution plan, such as Figure 4 shown; if the stop condition is not reached, step S4 is executed;

[0059] S4: Randomly select two individuals in the population to cross over to generate offspring;

[0060] S5: Calculate the similarity of the current individual;

[0061] S6: If the similarity between the individual and the existing individuals in the population is lower than th...

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Abstract

The invention relates to the field of intelligent logistics distribution, and particularly relates to an intelligent culture gene logistics distribution method based on similarity learning. The methodcomprises the following steps of uploading data such as cargo data, customer coordinates, road states and the like to a database; initializing a population; checking whether stopping conditions are met or not; performing roulette selection on two individuals in the population to perform crossing to generate offspring; calculating the similarity between the current individuals; if the similarity between the individuals and existing individuals in the population is lower than a threshold value, performing heuristic local searching on the individuals; and sorting the individuals in the population, thereby selecting out the optimal individuals. A similarity concept is adopted, so that the diversity of the population is controlled to a certain degree in an iteration process of an algorithm, the situation that the population is subject to premature convergence and falls into local optimum is avoided, the searching range of the population is greatly expanded, computing resources can be usedfor more potential solutions as more as possible through the population, and the optimization capability of the population is improved.

Description

technical field [0001] The invention relates to the field of intelligent logistics distribution, and more specifically, to an intelligent cultural gene logistics distribution method based on similarity learning. Background technique [0002] With the development of the Internet, e-commerce has penetrated more and more into people's lives, and the emergence of e-commerce has greatly promoted the development of the logistics industry. The logistics industry includes packaging, loading and unloading, transportation, distribution and other links. The network structure of logistics distribution directly determines the cost and efficiency of distribution. When the logistics industry was just emerging, the delivery staff generally determined the delivery route based on their daily accumulated experience. Without reasonable optimization, it often led to problems such as low delivery efficiency, unreasonable personnel allocation, and excessive delivery costs. [0003] The logistics...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q10/08
CPCG06Q10/047G06Q10/08355
Inventor 曾志伟曾碧
Owner GUANGDONG UNIV OF TECH
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