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Rescheduling Plan Optimization of Underground Mine Haulage Equipment Based on Random Breakdown Simulation

Author

Listed:
  • Ning Li

    (School of Resource and Environment Engineering, Wuhan University of Technology, Wuhan 430070, China
    Hubei Key Laboratory of Mineral Resources Processing and Environment, Wuhan 430070, China)

  • Shuzhao Feng

    (School of Resource and Environment Engineering, Wuhan University of Technology, Wuhan 430070, China)

  • Tao Lei

    (School of Resource and Environment Engineering, Wuhan University of Technology, Wuhan 430070, China
    Hubei Key Laboratory of Mineral Resources Processing and Environment, Wuhan 430070, China)

  • Haiwang Ye

    (School of Resource and Environment Engineering, Wuhan University of Technology, Wuhan 430070, China)

  • Qizhou Wang

    (School of Resource and Environment Engineering, Wuhan University of Technology, Wuhan 430070, China)

  • Liguan Wang

    (School of Resource and Safety Engineering, Central South University, Changsha 410083, China)

  • Mingtao Jia

    (School of Resource and Safety Engineering, Central South University, Changsha 410083, China)

Abstract

Due to production space and operating environment requirements, mine production equipment often breaks down, seriously affecting the mine’s production schedule. To ensure the smooth completion of the haulage operation plan under abnormal conditions, a model of the haulage equipment rescheduling plan based on the random simulation of equipment breakdowns is established in this paper. The model aims to accomplish both the maximum completion rate of the original mining plan and the minimum fluctuation of the ore grade during the rescheduling period. This model is optimized by improving the wolf colony algorithm and changing the location update formula of the individuals in the wolf colony. Then, the optimal model solution can be used to optimize the rescheduling of the haulage plan by considering equipment breakdowns. The application of the proposed method in an underground mine revealed that the completion rate of the mine’s daily mining plan reached 83.40% without increasing the amount of equipment, while the ore quality remained stable. Moreover, the improved optimization algorithm converged quickly and was characterized by high robustness.

Suggested Citation

  • Ning Li & Shuzhao Feng & Tao Lei & Haiwang Ye & Qizhou Wang & Liguan Wang & Mingtao Jia, 2022. "Rescheduling Plan Optimization of Underground Mine Haulage Equipment Based on Random Breakdown Simulation," Sustainability, MDPI, vol. 14(6), pages 1-18, March.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:6:p:3448-:d:771820
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    References listed on IDEAS

    as
    1. Al-Hinai, Nasr & ElMekkawy, T.Y., 2011. "Robust and stable flexible job shop scheduling with random machine breakdowns using a hybrid genetic algorithm," International Journal of Production Economics, Elsevier, vol. 132(2), pages 279-291, August.
    2. Foroughi, Sorayya & Hamidi, Jafar Khademi & Monjezi, Masoud & Nehring, Micah, 2019. "The integrated optimization of underground stope layout designing and production scheduling incorporating a non-dominated sorting genetic algorithm (NSGA-II)," Resources Policy, Elsevier, vol. 63(C), pages 1-1.
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    Cited by:

    1. Dževdet Halilović & Miloš Gligorić & Zoran Gligorić & Dragan Pamučar, 2023. "An Underground Mine Ore Pass System Optimization via Fuzzy 0–1 Linear Programming with Novel Torricelli–Simpson Ranking Function," Mathematics, MDPI, vol. 11(13), pages 1-35, June.

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