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Swarm intelligence-based framework for accelerated and optimized assembly line design in the automotive industry

Author

Listed:
  • Anass Houd

    (University of Franche-Comte
    Forvia Clean Mobility)

  • Benoit Piranda

    (University of Franche-Comte)

  • Raphael Matos

    (Forvia Clean Mobility)

  • Julien Bourgeois

    (University of Franche-Comte)

Abstract

This study proposes a dynamic simulation-based framework that utilizes swarm intelligence algorithms to optimize the design of hybrid assembly lines in the automotive industry. Two recent discrete versions of Whale Optimization Algorithm (named VNS-DWOA) and Gorilla Troops Optimizer (named DGTO) were developed to solve the assembly line balancing problem. The effectiveness of these algorithms was compared to six conventional meta-heuristics as well as the solution proposed by process design experts. The experimental results show that our methods outperform the conventional meta-heuristics and achieve comparable or better results than the experts’ solution. Particulary, VNS-DWOA, being the top performer, has consistently provided averagely remarkable enhancements of cycle time, ranging from 7% when compared to the process expert’s solution to 20% maximum improvement compared to all other methods. The findings of this study highlight the effectiveness of utilizing swarm intelligence algorithms and dynamic simulation-based frameworks as well as the potential benefits of implementing these digital methods in industrial settings, as they can significantly accelerate and enhance the optimization of assembly line design particularly and reduce time to market generally.

Suggested Citation

  • Anass Houd & Benoit Piranda & Raphael Matos & Julien Bourgeois, 2024. "Swarm intelligence-based framework for accelerated and optimized assembly line design in the automotive industry," Journal of Intelligent Manufacturing, Springer, vol. 35(6), pages 2829-2843, August.
    • Handle: RePEc:spr:joinma:v:35:y:2024:i:6:d:10.1007_s10845-023-02182-5
    DOI: 10.1007/s10845-023-02182-5
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