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Research on Multi-objective Balancing Optimization of Truck Mixed Assembly Line Based on Improved Genetic Algorithm
Kun Yang a, Shuaipeng Wu b, Yibo Wang c and Jian Zou d
aAssociate Professor, College of Mechanical Engineering and Automation, Liaoning University of Technology, Jinzhou, 121000, China.
bGraduate Student, College of Mechanical Engineering and Automation, Liaoning University of Technology, Jinzhou, 121000, China.
cLecturer, College of Mechanical Engineering and Automation, Liaoning University of Technology, Jinzhou, 121000, China.
dLaboratory Master, College of Mechanical and Power Engineering, Yingkou Institute of Technology, 115014, China
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Abstract:
A corresponding multi-objective mathematical model has been established to address the issue of low production capacity in the mixed-model assembly line of a certain automobile company for trucks. The problems of slow convergence speed and easy entrapment in local optimal solutions of the standard genetic algorithm are addressed by improving the genetic algorithm. Selecting examples from the literature to test the performance of the improved genetic algorithm had been done, and Matlab had been used for programming and solving. The results showed that the workload decreased from 0.653 to 0.513, worker costs dropped from 1,872 yuan to 1,786 yuan, and the operation time of the improved genetic algorithm was reduced by 37%. A mixed-model assembly line simulation was created using Flexsim simulation software. The results indicated that the disparity in utilization rate among workplaces decreased to 12.25%, and production output increased from 1,000 units to 1,190 units. The balance optimization was carried out for a specific truck assembly line. The results indicate that the number of workplaces decreased from 26 to 23, the workload dropped from 15.36 to 11.27, and worker costs fell from 7,138 yuan to 6,257 yuan. These findings suggest that the selected optimization objectives and the improved genetic algorithm are effective in addressing multi-objective balancing problems in mixed-model assembly lines.
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Keywords: Mixed flow assembly line, Multi-objective optimization, Genetic algorithm, Assembly line balancing, Flexsim.
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©
2026
CSME , ISSN 0257-9731
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