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A sequence learning harmony search algorithm for the flexible process planning problem

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  • Kaiping Luo

Abstract

Flexible process planning involves selecting and sequencing the requisite operations, and assigning the right machine, tool and access direction to each selected operation for minimising the production cost or the completion time. It is one of the challenging combinatorial optimisation problems due to sequencing flexibility, processing flexibility and operation flexibility. A sequence learning harmony search algorithm is accordingly proposed. Distinctively, the well-designed algorithm searches for the optimal process plan by intelligently finding the proper immediate successor for each selected operation in turn rather than resorting to the common shifting and swapping operators in sequencing. The innovative algorithm does not also require extra efforts to plot the operational precedence graph or the AND/OR-network graph. The experimental results indicate that the proposed algorithm significantly outperforms other heuristics in terms of the quality of solution found and the convergence rate of the algorithm. For the large-scale complicated instances, the proposed algorithm establishes a challenging flag.

Suggested Citation

  • Kaiping Luo, 2022. "A sequence learning harmony search algorithm for the flexible process planning problem," International Journal of Production Research, Taylor & Francis Journals, vol. 60(10), pages 3182-3200, May.
  • Handle: RePEc:taf:tprsxx:v:60:y:2022:i:10:p:3182-3200
    DOI: 10.1080/00207543.2021.1912432
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    Cited by:

    1. Luo, Kaiping & Shen, Guangya & Li, Liheng & Sun, Jianfei, 2023. "0-1 mathematical programming models for flexible process planning," European Journal of Operational Research, Elsevier, vol. 308(3), pages 1160-1175.

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