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Hybrid Flow-Shop Scheduling Problems with Missing and Re-Entrant Operations Considering Process Scheduling and Production of Energy Consumption

Author

Listed:
  • Hongtao Tang

    (Institute of Industrial Engineering, College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China)

  • Jiahao Zhou

    (Institute of Industrial Engineering, College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China)

  • Yiping Shao

    (Institute of Industrial Engineering, College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China)

  • Zhixiong Yang

    (Jiaxing Sudoku Bridge Technology Co., Ltd., Jiaxing 314599, China)

Abstract

A hybrid flow shop scheduling model with missing and re-entrant operations was designed to minimize the maximum completion time and the reduction in energy consumption. The proposed dual-population genetic algorithm was enhanced with a range of improvements, which include the design of a three-layer gene coding method, hierarchical crossover and mutation techniques, and the development of an adaptive operator that considered gene similarity and chromosome fitness values. The optimal and worst individuals were exchanged between the two subpopulations to improve the exploration ability of the algorithm. An orthogonal experiment was performed to obtain the optimal horizontal parameter set of the algorithm. Furthermore, an experiment was conducted to compare the proposed algorithm with a basic genetic algorithm, particle swarm optimization algorithm, and ant colony optimization, which were all performed on the same scale. The experimental results show that the fitness value of the proposed algorithm is above 15% stronger than the other 4 algorithms on a small scale, and was more than 10% stronger than the other 4 algorithms on a medium and large scale. Under the condition close to the actual scale, the results of ten repeated calculations showed that the proposed algorithm had higher robustness.

Suggested Citation

  • Hongtao Tang & Jiahao Zhou & Yiping Shao & Zhixiong Yang, 2023. "Hybrid Flow-Shop Scheduling Problems with Missing and Re-Entrant Operations Considering Process Scheduling and Production of Energy Consumption," Sustainability, MDPI, vol. 15(10), pages 1-19, May.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:10:p:7982-:d:1146268
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    References listed on IDEAS

    as
    1. W. Qin & J. Zhang & D. Song, 2018. "An improved ant colony algorithm for dynamic hybrid flow shop scheduling with uncertain processing time," Journal of Intelligent Manufacturing, Springer, vol. 29(4), pages 891-904, April.
    2. S. M. Mousavi & I. Mahdavi & J. Rezaeian & M. Zandieh, 2018. "An efficient bi-objective algorithm to solve re-entrant hybrid flow shop scheduling with learning effect and setup times," Operational Research, Springer, vol. 18(1), pages 123-158, April.
    3. Xiang Yi Zhang & Lu Chen, 2018. "A re-entrant hybrid flow shop scheduling problem with machine eligibility constraints," International Journal of Production Research, Taylor & Francis Journals, vol. 56(16), pages 5293-5305, August.
    4. M.K. Marichelvam & T. Prabaharan, 2014. "Performance evaluation of an improved hybrid genetic scatter search (IHGSS) algorithm for multistage hybrid flow shop scheduling problems with missing operations," International Journal of Industrial and Systems Engineering, Inderscience Enterprises Ltd, vol. 16(1), pages 120-141.
    5. Hamidreza Eskandari & Amirhamed Hosseinzadeh, 2014. "A variable neighbourhood search for hybrid flow-shop scheduling problem with rework and set-up times," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 65(8), pages 1221-1231, August.
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