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Method for solving job-shop scheduling problem based on data-driven improved genetic algorithm

A technology for improving genetic algorithm and job shop, which is applied in the field of solving job shop scheduling problems based on data-driven improved genetic algorithm, and can solve problems such as ignoring potential laws of historical scheduling data.

Active Publication Date: 2021-06-08
ZHENGZHOU UNIVERSITY OF LIGHT INDUSTRY
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Problems solved by technology

However, the research on traditional job shop scheduling problems often ignores the potential laws of historical scheduling data in industrial production.

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  • Method for solving job-shop scheduling problem based on data-driven improved genetic algorithm
  • Method for solving job-shop scheduling problem based on data-driven improved genetic algorithm
  • Method for solving job-shop scheduling problem based on data-driven improved genetic algorithm

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[0068] The specification and claims do not use the difference in name as a way to distinguish components, but use the difference in function of components as a criterion for distinguishing. As mentioned throughout the specification and claims, "comprising" is an open term, so it should be interpreted as "including but not limited to". "Approximately" means that within an acceptable error range, those skilled in the art can solve the technical problem within a certain error range and basically achieve the technical effect.

[0069] The direction terms such as up, down, left, and right in this specification and claims are combined with the drawings for further explanation, making this application easier to understand, and not limiting this application. In different scenarios, up and down, left and right, and inside and outside are all Relatively speaking.

[0070] The present invention will be described in further detail below in conjunction with the accompanying drawings.

[...

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Abstract

The invention provides a method for solving a job-shop scheduling problem through a data-driven improved genetic algorithm (AR-GA), and effectively solves the problems that when solving the job-shop scheduling problem, a traditional genetic algorithm is limited by initial population quality, the convergence speed is low, and the problem is prone to falling into a local optimal solution. The algorithm is characterized in that frequent process blocks in a gene sequence and the position and probability of each gene appearing in each process are obtained by means of an association rule and a combined scheduling rule in the population initialization stage of the genetic algorithm; in the crossing stage, three different crossing modes are designed according to the distribution of the frequent process blocks in the population to be crossed; in the variation process, the subsection Hamming distance is combined to guide the variation of the offspring population, and the information of the frequent process block is updated after each iteration. The improved algorithm provided by the invention not only has an advantage in solving quality, but also has a certain advantage in solving the job shop scheduling problem of efficiency and stability.

Description

technical field [0001] The invention relates to the technical field of job shop scheduling, and more specifically, relates to a method for solving job shop scheduling problems based on data-driven improved genetic algorithms. Background technique [0002] The arrival of a new round of industrial revolution has prompted the rapid integration of information technology into the industrial manufacturing process, so that enterprises and factories have accumulated a large amount of production scheduling data, which presents characteristics such as irregularity and diversity. However, the research on traditional job shop scheduling problems often ignores the potential laws of historical scheduling data in industrial production. Therefore, it is of great significance to study the influence of data-driven in the application research of solving the job shop scheduling problem. Contents of the invention [0003] In order to solve the above-mentioned technical problems, the present i...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/06G06Q50/04G06N3/12
CPCG06Q10/06316G06Q50/04G06N3/126Y02P90/30
Inventor 乔东平柏文通段绿旗王雅静肖艳秋文笑雨李浩李立伟孙春亚张玉彦王昊琪
Owner ZHENGZHOU UNIVERSITY OF LIGHT INDUSTRY
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