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Multi-objective flow shop scheduling system based on wireless network genetic algorithm from perspective of artificial intelligence

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Abstract

With the continuous development of the economy, various enterprises pay more and more attention to the scheduling problem of multi-objective workshops. The multi-objective flow shop scheduling problem is extremely widely used in real generation. Reasonable use of workshop resources and assignment of work tasks can improve the work efficiency of workshops and increase work income. The existing multi-objective flow workshop has the problems of high scheduling risk index and low work efficiency. The multi-objective optimization problem solving method in the wireless network genetic algorithm is used to solve this practical problem, and the optimization is used to transform the operators from single task to multi-task direction, and arrange the working time and sequence according to certain rules, so as to achieve the purpose of efficient production. In the field of artificial intelligence, this paper used the wireless network genetic algorithm (GA) to design a multi-objective flow shop scheduling analysis system, analyzed and adjusted the scheduling problems of the workshop, and completed the scheduling design of the entire workshop. Through experimental tests on different workshops: workshop machine error test, workshop work time consumption test, work efficiency test and workshop worker satisfaction test, it is found that the use of wireless network GA for multi-objective workshop flow scheduling can greatly reduce the working error of the workshop. It can reduce the time-consuming work of the workshop, and the wireless network genetic algorithm can improve the work efficiency of the assembly line. The multi-objective flow scheduling based on the wireless network GA improves the worker’s satisfaction by 10.7%.

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Data availability statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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The author(s) received no financial support for the research, authorship, and/or publication of this article.

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Authors and Affiliations

  1. College of Mechanical Engineering, Dalian Jiaotong University, Dalian, 116028, Liaoning, China

    Fei Wang

  2. Department of Basics, Dalian University of Science and Technology, Dalian, 116052, Liaoning, China

    Fei Wang

  3. College of Foreign Languages, Dalian University of Science and Technology, Dalian, 116052, Liaoning, China

    Si Wu

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  1. Fei Wang
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  2. Si Wu
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Correspondence to Si Wu.

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Wang, F., Wu, S. Multi-objective flow shop scheduling system based on wireless network genetic algorithm from perspective of artificial intelligence. Soft Comput (2023). https://doi.org/10.1007/s00500-023-08364-w

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