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A modified particle swarm optimization algorithm to mixed-model two-sided assembly line balancing

Author

Listed:
  • Yılmaz Delice

    (Erciyes University)

  • Emel Kızılkaya Aydoğan

    (Erciyes University)

  • Uğur Özcan

    (Gazi University)

  • Mehmet Sıtkı İlkay

    (Erciyes University)

Abstract

In this paper, a new modified particle swarm optimization algorithm with negative knowledge is proposed to solve the mixed-model two-sided assembly line balancing problem. The proposed approach includes new procedures such as generation procedure which is based on combined selection mechanism and decoding procedure. These new procedures enhance the solution capability of the algorithm while enabling it to search at different points of the solution space, efficiently. Performance of the proposed approach is tested on a set of test problem. The experimental results show that the proposed approach can be acquired distinguished results than the existing solution approaches.

Suggested Citation

  • Yılmaz Delice & Emel Kızılkaya Aydoğan & Uğur Özcan & Mehmet Sıtkı İlkay, 2017. "A modified particle swarm optimization algorithm to mixed-model two-sided assembly line balancing," Journal of Intelligent Manufacturing, Springer, vol. 28(1), pages 23-36, January.
  • Handle: RePEc:spr:joinma:v:28:y:2017:i:1:d:10.1007_s10845-014-0959-7
    DOI: 10.1007/s10845-014-0959-7
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    References listed on IDEAS

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    8. Minghai Yuan & Hongyan Yu & Jinting Huang & Aimin Ji, 2019. "Reconfigurable assembly line balancing for cloud manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 30(6), pages 2391-2405, August.
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