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Production and manufacturing scheduling optimization method based on improved genetic algorithm

A technology for improving genetic algorithms and optimization methods, applied in genetic rules, manufacturing computing systems, computing, etc., can solve the problems that genetic algorithms cannot perform global optimization and reduce optimization effects

Inactive Publication Date: 2021-09-10
ZHEJIANG UNIV
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  • Abstract
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Problems solved by technology

However, these solutions are artificially added with some constraints, which will cause the genetic algorithm to be unable to perform global optimization, greatly reducing the optimization effect

Method used

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  • Production and manufacturing scheduling optimization method based on improved genetic algorithm
  • Production and manufacturing scheduling optimization method based on improved genetic algorithm
  • Production and manufacturing scheduling optimization method based on improved genetic algorithm

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Embodiment

[0036] Under the conditions of fierce market competition and the general trend of economic globalization, enterprises are facing more and more complex and diversified market demands. Improving production efficiency and responding to market demands in a timely manner are the core goals of modern intelligent manufacturing. An important way to improve efficiency and enhance the core competitiveness of enterprises. The manufacturing process is complicated, the processing route and the use of equipment are very flexible, and there are many changes in product design, processing demand and order quantity. In order to achieve the goal of modern intelligent manufacturing, it is necessary to optimize the production scheduling of manufacturing enterprises and arrange them reasonably Production and processing plan.

[0037] Using the genetic algorithm to solve the production scheduling optimization problem will produce coding conflicts, that is, it cannot satisfy the constraint that the s...

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Abstract

The invention discloses a production and manufacturing scheduling optimization method based on an improved genetic algorithm. The method comprises the following steps: firstly, aiming at a production scheduling optimization target, establishing a mathematical model, determining a population fitness function, reading order information and equipment information, numbering equipment, encoding an order process into a chromosome gene, initializing a population, the maximum number of iterations and a production scheduling matrix; carrying out operations such as crossover and variation on chromosomes of the population to obtain a new generation of population, arranging order procedures corresponding to chromosome genes on equipment in a conflict-free manner by combining a production scheduling matrix, and calculating a fitness function of population individuals; and finally, selecting next-generation individuals according to a binary tournament selection strategy, reserving fitness individuals to the next generation, repeating the steps until the maximum iteration frequency is reached, and decoding the individual chromosome with the highest fitness as an optimal production scheduling scheme. The global optimization can be quickly realized, and the effect is better than that of the existing optimization method.

Description

technical field [0001] The invention belongs to the field of industrial process monitoring, modeling and optimization, and in particular relates to an optimization method for production scheduling based on an improved genetic algorithm. Background technique [0002] Under the conditions of fierce market competition and the general trend of economic globalization, enterprises are facing more and more complex and diversified market demands. Improving production efficiency and responding to market demands in a timely manner are the core goals of modern intelligent manufacturing. An important way to improve efficiency and enhance the core competitiveness of enterprises. The manufacturing process is complicated, the processing route and the use of equipment are very flexible, and there are many changes in product design, processing demand and order quantity. In order to achieve the goal of modern intelligent manufacturing, it is necessary to optimize the production scheduling of ...

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Application Information

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IPC IPC(8): G06Q10/02G06Q50/04G06N3/12
CPCG06Q10/02G06Q50/04G06N3/126Y02P90/30
Inventor 赵建华杨春节李丹宁
Owner ZHEJIANG UNIV
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