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Dispatching method based on particle swarm optimization for make-to-availability

Author

Listed:
  • Robson Flavio Castro

    (Federal University of São Carlos – UFSCAR)

  • Moacir Godinho-Filho

    (Federal University of São Carlos – UFSCAR)

  • Roberto Fernandes Tavares-Neto

    (Federal University of São Carlos – UFSCAR)

Abstract

Make-to-availability (MTA) is a subtype of make-to-stock that emerged from production, planning, and control system, simplified drum-buffer-rope (S-DBR). The dispatching production order logic of the MTA does not consider the elements present in a wide range of manufacturing systems, such as sequence-dependent setup time. These characteristics generally creates difficulties in the S-DBR, thereby worsening performance indicators, such as mean flow time, setup time, and stock replenishment frequency. Given this research gap, the present study aims to develop a dispatching method for production orders in MTA, based on the particle swarm optimization (PSO) metaheuristic. The dispatching method aims to minimize the mean flow time, setup time, and stock levels in environments with a dependent setup time. To evaluate the performance of the new dispatching method, we used computational simulation to compare this method and the MTA dispatching logic. The results showed that the PSO for sequence achieved better performance, reducing the mean flow time, setup time, and stock level.

Suggested Citation

  • Robson Flavio Castro & Moacir Godinho-Filho & Roberto Fernandes Tavares-Neto, 2022. "Dispatching method based on particle swarm optimization for make-to-availability," Journal of Intelligent Manufacturing, Springer, vol. 33(4), pages 1021-1030, April.
    • Handle: RePEc:spr:joinma:v:33:y:2022:i:4:d:10.1007_s10845-020-01707-6
    DOI: 10.1007/s10845-020-01707-6
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    References listed on IDEAS

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    1. Maroua Nouiri & Abdelghani Bekrar & Abderezak Jemai & Smail Niar & Ahmed Chiheb Ammari, 2018. "An effective and distributed particle swarm optimization algorithm for flexible job-shop scheduling problem," Journal of Intelligent Manufacturing, Springer, vol. 29(3), pages 603-615, March.
    2. 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.
    3. Heng Zhang & Utpal Roy, 2019. "A semantics-based dispatching rule selection approach for job shop scheduling," Journal of Intelligent Manufacturing, Springer, vol. 30(7), pages 2759-2779, October.
    4. Drexl, A. & Kimms, A., 1997. "Lot sizing and scheduling -- Survey and extensions," European Journal of Operational Research, Elsevier, vol. 99(2), pages 221-235, June.
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