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Balancing Optimization of Mixed-Flow Assembly Line Based on Hybrid Genetic Algorithm

In: Liss 2020

Author

Listed:
  • Meng Li

    (Beijing Jiaotong University)

  • Dan Chang

    (Beijing Jiaotong University)

Abstract

The balancing problem of the assembly line is a typical NP-hard problem and one of the most important problems to be solved by manufacturing companies. For the disadvantages that genetic algorithms are prone to local optimal solutions and precociousness in the optimization process, the article focuses on the second type of balancing issues for mixed-flow assembly lines. A hybrid genetic algorithm was designed and constructed to combine three evaluation metrics: the smoothing index, the equilibrium loss coefficient and the imbalance coefficient of the adaptation function, combining the simulated annealing algorithm with a genetic algorithm to speed up convergence to obtain a global optimal solution. Finally, a mixed-flow assembly line of Company L is used as an example to solve the equilibrium of the assembly line using the designed hybrid genetic algorithm for improvement and optimization. And also demonstrates that the method is suitable for solving balance problems in mixed-flow assembly lines.

Suggested Citation

  • Meng Li & Dan Chang, 2021. "Balancing Optimization of Mixed-Flow Assembly Line Based on Hybrid Genetic Algorithm," Springer Books, in: Shifeng Liu & Gábor Bohács & Xianliang Shi & Xiaopu Shang & Anqiang Huang (ed.), Liss 2020, pages 931-946, Springer.
    • Handle: RePEc:spr:sprchp:978-981-33-4359-7_64
    DOI: 10.1007/978-981-33-4359-7_64
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    Cited by:

    1. Sikora, Celso Gustavo Stall, 2024. "Balancing mixed-model assembly lines for random sequences," European Journal of Operational Research, Elsevier, vol. 314(2), pages 597-611.

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