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An evolutionary clustering search for the total tardiness blocking flow shop problem

Author

Listed:
  • Marcelo Seido Nagano

    (University of São Paulo)

  • Adriano Seiko Komesu

    (University of São Paulo)

  • Hugo Hissashi Miyata

    (University of São Paulo)

Abstract

In this paper we propose the evolutionary clustering search (ECS) algorithm combined with a variable neighbourhood search (VNS) for the m-machine blocking flow shop scheduling problem with total tardiness minimization. The proposed ECS uses NEH-based procedure to generate an initial solution, the genetic algorithm to generate solutions and VNS to improve the solutions. A set of experiments were carried out to adjust the parameter of the metaheuristic. ECS is compared with ILS by Ribas et al. (J Prod Res 51(17):5238–5252, 2013), known as the best metaheuristic for the problem. Computational tests show superiority of the new method for the set of problems evaluated. Finally, we update 67 new best values of best-know found by ECS.

Suggested Citation

  • Marcelo Seido Nagano & Adriano Seiko Komesu & Hugo Hissashi Miyata, 2019. "An evolutionary clustering search for the total tardiness blocking flow shop problem," Journal of Intelligent Manufacturing, Springer, vol. 30(4), pages 1843-1857, April.
    • Handle: RePEc:spr:joinma:v:30:y:2019:i:4:d:10.1007_s10845-017-1358-7
    DOI: 10.1007/s10845-017-1358-7
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    References listed on IDEAS

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    Cited by:

    1. Ying Sun & Jeng-Shyang Pan & Pei Hu & Shu-Chuan Chu, 2023. "Enhanced Equilibrium Optimizer algorithm applied in job shop scheduling problem," Journal of Intelligent Manufacturing, Springer, vol. 34(4), pages 1639-1665, April.
    2. Xiaohui Zhang & Xinhua Liu & Shufeng Tang & Grzegorz Królczyk & Zhixiong Li, 2019. "Solving Scheduling Problem in a Distributed Manufacturing System Using a Discrete Fruit Fly Optimization Algorithm," Energies, MDPI, vol. 12(17), pages 1-24, August.

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