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Publishing Language: Chinese

Die Electrode Scheduling Problem Solution Based on Genetic Algorithm

Qinxiang XIA1( )Kai LI1Jun MA2Xiuquan CHENG3Gangfeng XIAO1
School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510640, Guangdong, China
Zhuhai Gree Precision Mold Company, Zhuhai 519070, Guangdong, China
Aircraft Maintenance Engineering College, Guangzhou Civil Aviation College, Guangzhou 510403, Guangdong, China
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Abstract

In view of the scheduling problem of die electrode in CNC and EDM stages, a mathematical model with batch processing and correlation characteristics was established. To minimize the tardiness of die parts, the solving process of die electrode scheduling problem was divided into two stages: batch processing and batch scheduling. In the first stage, the batch processing problem was solved according to the principle of correlation, and the correlation priority batch algorithm was designed. In the second stage, the batch scheduling problem was solved by genetic algorithm, and a strategy based on animal breeding was proposed to improve the traditional genetic algorithm. The die electrode scheduling program was developed based on MATLAB software to realize the above two stages of solving process, and the test was carried out under 24 kinds of simulation examples. The results show that, the designed die electrode scheduling algorithm is effective for solving the batch processing and batch scheduling problem of die electrode; the proposed strategy of breeding can significantly improve the quality of traditional genetic algorithm solution; and the tardiness of die parts in the examples can be reduced by 16.71% at most.

CLC number: TH18;TP3 Article ID: 1000-565X(2022)03-0080-08

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Journal of South China University of Technology (Natural Science Edition)
Pages 80-87

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Cite this article:
XIA Q, LI K, MA J, et al. Die Electrode Scheduling Problem Solution Based on Genetic Algorithm. Journal of South China University of Technology (Natural Science Edition), 2022, 50(3): 80-87. https://doi.org/10.12141/j.issn.1000-565X.210284

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Received: 10 May 2021
Published: 25 March 2022
© Journal of South China University of Technology(Natural Science Edition)