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Article type: Research Article
Authors: Zhang, Yang
Affiliations: School of Artificial Intelligence Application, Shanghai Urban Construction Vocational College, Shanghai 201415, China | E-mail: [email protected]
Correspondence: [*] Corresponding author: School of Artificial Intelligence Application, Shanghai Urban Construction Vocational College, Shanghai 201415, China. E-mail: [email protected].
Abstract: With the development of the world consumption economy, the demand of the society on the production efficiency of the workshop is increasing. In order to improve the production efficiency of the industrial robot automatic production line, the working mathematical model of the industrial robot automatic production line is built, and the improved genetic algorithm is used to optimize the layout of the industrial robot automatic production line. The simulation results show that, compared with the selected design method based on a standardized production line and the optimization method based on genetic algorithm, the layout production scheme output by the improved genetic algorithm has higher space utilization efficiency, shorter material handling distance in the workshop, and better overall layout effect. When the experimental time is 14 units, the space utilization rate of the output solution of the improved genetic algorithm is 94%, and the index of the other methods is lower than 70%. Moreover, the material handling distance of the former is the closest to the straight line, and the zoning planning of production equipment and materials is the most reasonable. The experimental data show that the layout optimization method of industrial robot automatic production line based on improved genetic algorithm can effectively improve the planning quality of the production line and improve the production efficiency.
Keywords: Genetic algorithm, industrial robot, production line layout, availability model, fitness model, task assignment
DOI: 10.3233/JCM-226557
Journal: Journal of Computational Methods in Sciences and Engineering, vol. 23, no. 1, pp. 469-484, 2023
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