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A novel approach in selective assembly with an arbitrary distribution to minimize clearance variation using evolutionary algorithms: a comparative study

Author

Listed:
  • Lenin Nagarajan

    (Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology)

  • Siva Kumar Mahalingam

    (Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology)

  • Jayakrishna Kandasamy

    (VIT University)

  • Selvakumar Gurusamy

    (Sri Sivasubramaniya Nadar College of Engineering)

Abstract

The minimization of surplus components with normal dimensional distributions while making selective assemblies was the only objective considered in the previous research works carried out by various researchers in different periods. Seldom works have been found on selective assembly by considering all dimensional distributions. In this proposed work, a novel method is developed for making assemblies with zero surplus components and minimum clearance variation by considering arbitrary distribution, to demonstrate the greater improvement in the results than the past literature. Krill Herd algorithm has been implemented for identifying the best combination of groups. Computational results showed that the proposed krill herd algorithm outperformed as compared with existing literature and as well as the results by gaining-sharing knowledge-based algorithm, differential evolution algorithm, and particle swarm optimization algorithm.

Suggested Citation

  • Lenin Nagarajan & Siva Kumar Mahalingam & Jayakrishna Kandasamy & Selvakumar Gurusamy, 2022. "A novel approach in selective assembly with an arbitrary distribution to minimize clearance variation using evolutionary algorithms: a comparative study," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1337-1354, June.
    • Handle: RePEc:spr:joinma:v:33:y:2022:i:5:d:10.1007_s10845-020-01720-9
    DOI: 10.1007/s10845-020-01720-9
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    References listed on IDEAS

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    1. Mahdi S. Alajmi & Fawzan S. Alfares & Mohamed S. Alfares, 2019. "Selection of optimal conditions in the surface grinding process using the quantum based optimisation method," Journal of Intelligent Manufacturing, Springer, vol. 30(3), pages 1469-1481, March.
    2. Ivona Brajević & Jelena Ignjatović, 2019. "An upgraded firefly algorithm with feasibility-based rules for constrained engineering optimization problems," Journal of Intelligent Manufacturing, Springer, vol. 30(6), pages 2545-2574, August.
    3. S.M. Kannan & R. Sivasubramanian & V. Jayabalan, 2009. "A new method in selective assembly for components with skewed distributions," International Journal of Productivity and Quality Management, Inderscience Enterprises Ltd, vol. 4(5/6), pages 569-589.
    4. Hao Liu & Yue Wang & Liangping Tu & Guiyan Ding & Yuhan Hu, 2019. "A modified particle swarm optimization for large-scale numerical optimizations and engineering design problems," Journal of Intelligent Manufacturing, Springer, vol. 30(6), pages 2407-2433, August.
    5. James T. Lin & Chun-Chih Chiu, 2018. "A hybrid particle swarm optimization with local search for stochastic resource allocation problem," Journal of Intelligent Manufacturing, Springer, vol. 29(3), pages 481-495, March.
    6. Choo Jun Tan & Siew Chin Neoh & Chee Peng Lim & Samer Hanoun & Wai Peng Wong & Chu Kong Loo & Li Zhang & Saeid Nahavandi, 2019. "Application of an evolutionary algorithm-based ensemble model to job-shop scheduling," Journal of Intelligent Manufacturing, Springer, vol. 30(2), pages 879-890, February.
    7. J. Rajesh Babu & A. Asha, 2015. "Modelling in selective assembly with symmetrical interval-based Taguchi loss function for minimising assembly loss and clearance variation," International Journal of Manufacturing Technology and Management, Inderscience Enterprises Ltd, vol. 29(5/6), pages 288-308.
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