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Mathematical modelling and a discrete cuckoo search particle swarm optimization algorithm for mixed model sequencing problem with interval task times

Author

Listed:
  • Jiahua Zhang

    (Wuxi Vocational Institute of Arts and Technology)

  • Xuemei Liu

    (Tongji University)

  • Beikun Zhang

    (BYD Auto Industry Company Limited)

Abstract

This paper addresses a sequencing problem with uncertain task times in mixed model assembly lines. In this problem, task times are not known exactly but are given by intervals of their possible values. A mixed integer non-linear programming model is developed to minimize the utility work time, which is converted into a mixed integer linear programming (MILP) model to deal with small-sized instances optimally. Due to the NP-hardness of the problem, a discrete cuckoo search particle swarm optimization (DCSPSO) algorithm is developed. In the proposed algorithm, a particle position is updated by crossover and mutation operators in the discrete domain and discrete Levy flight is used to improve the solution quality further. Numerical experiments are conducted on the designed instances. The results indicate that the DCSPSO algorithm outperforms the exact method and the other three meta-heuristic algorithms. A case study of engine cylinder heads sequencing problem shows the proposed approach can obtain multiple solutions for decision-makers to choose according to the actual situation.

Suggested Citation

  • Jiahua Zhang & Xuemei Liu & Beikun Zhang, 2024. "Mathematical modelling and a discrete cuckoo search particle swarm optimization algorithm for mixed model sequencing problem with interval task times," Journal of Intelligent Manufacturing, Springer, vol. 35(8), pages 3837-3856, December.
    • Handle: RePEc:spr:joinma:v:35:y:2024:i:8:d:10.1007_s10845-023-02300-3
    DOI: 10.1007/s10845-023-02300-3
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    References listed on IDEAS

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    1. Alexandre Dolgui & Hichem Haddou Benderbal & Fabio Sgarbossa & Simon Thevenin, 2024. "Editorial for the special issue: AI and data-driven decisions in manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 35(8), pages 3599-3604, December.

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