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Intelligent transportation scheduling management method based on neural network genetic algorithm

A neural network and scheduling management technology, which is applied in the field of large-diameter and ultra-thick seamless tee fittings for high-pressure hydrogenation, can solve problems such as difficulty in convergence, low computational efficiency, and premature convergence.

Active Publication Date: 2020-03-31
江苏佳利达国际物流股份有限公司
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The fitness function is similar to the role of the environment in the evolution of organisms. Individuals with high fitness will produce more offspring in the reproduction process from generation to generation, while individuals with low fitness will gradually die out; but the computational efficiency of genetic algorithm Low, easy to fall into local optimum, difficult convergence and other deficiencies, which will cause premature convergence, or a large number of iterative recalculations. In the modern logistics industry, the logistics volume is increasing, which is very consistent with the basic coding of genetic algorithms, and from disorder However, how to dispatch vehicles quickly and efficiently with as few dispatch vehicles as possible is obviously a difficult problem, which cannot be solved by genetic algorithms

Method used

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  • Intelligent transportation scheduling management method based on neural network genetic algorithm
  • Intelligent transportation scheduling management method based on neural network genetic algorithm
  • Intelligent transportation scheduling management method based on neural network genetic algorithm

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Embodiment 1

[0069] Embodiment 1: as Figure 1-3 Shown: the intelligent transportation scheduling management method based on neural network genetic algorithm of the present embodiment, comprises the following steps:

[0070] A1. Set initialization parameters, establish a data module according to the transportation scheduling management system, and use a transportation plan of materials as a chromosome, and the parameters include information on the delivery point, transfer station information, receiving point information, transportation tool information, and material information A gene as a chromosome; its associated symbols are represented as follows:

[0071] n: delivery point, including {n1, n2, n3...n};

[0072] m: receiving point, including {m1, m2, m3...m};

[0073] In the actual logistics scheduling, the shipping point and the receiving point are in the same set, that is, the shipping point is also used as the receiving point, and the receiving point is also used as the shipping po...

Embodiment 2

[0123] Embodiment 2: This embodiment is basically the same as Embodiment 1, except that the population after each iteration in the genetic algorithm is added to the previous population for calculation to avoid premature convergence and inaccurate data. For example, add the population obtained after the operation of G1 into G1 to obtain population G2, and iterate accordingly.

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Abstract

The invention discloses an intelligent transportation scheduling management method based on a neural network genetic algorithm. The method comprises the following steps: A1, establishing a data moduleaccording to a transportation scheduling management system; a2, generating an initial population G, wherein the initial population comprises N chromosomes, and the chromosome encodes the material M (Ma and / or Mb) according to the characteristics selected as required according to any length; constructing a neural network, wherein the delivery point is a neural network input node, the receiving point is an output node, and the transfer station is a hidden layer; a4, starting iteration. A neural network algorithm is added into the genetic algorithm, so that the defects of low calculation efficiency, easiness in falling into local optimum, difficulty in convergence and the like in the genetic algorithm are avoided, and premature convergence or a large number of iterative complex calculationsare avoided. The fitness evaluation function evaluates from two opposite angles of economic applicability e and time length t, and the two opposite functions are balanced with each other, so that a rapid and economic logistics scheduling scheme is realized.

Description

technical field [0001] The invention relates to the technical field of transportation pipelines, in particular to a high-pressure hydrogenation large-diameter super-thick seamless tee pipe fitting. Background technique [0002] The genetic algorithm is based on the principle of survival of the fittest in self-science, and was later cited in the optimization algorithm. The genetic operations performed in the evolution process include coding, selection, crossover, mutation, and survival of the fittest. There is no need for function derivatives and requirements The function is continuous. It simulates the phenomena of reproduction, crossover and gene mutation in the process of natural selection and natural inheritance. In each iteration, a group of candidate solutions is reserved, and a better individual is selected from the solution group according to a certain index. Genetic operators (selection, crossover and mutation) combine these individuals to generate a new generation o...

Claims

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Application Information

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IPC IPC(8): G06Q10/08G06N3/12
CPCG06N3/126G06Q10/08355
Inventor 潘红斌
Owner 江苏佳利达国际物流股份有限公司
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