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Intelligent Scheduling for Underground Mobile Mining Equipment

Author

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  • Zhen Song
  • Håkan Schunnesson
  • Mikael Rinne
  • John Sturgul

Abstract

Many studies have been carried out and many commercial software applications have been developed to improve the performances of surface mining operations, especially for the loader-trucks cycle of surface mining. However, there have been quite few studies aiming to improve the mining process of underground mines. In underground mines, mobile mining equipment is mostly scheduled instinctively, without theoretical support for these decisions. Furthermore, in case of unexpected events, it is hard for miners to rapidly find solutions to reschedule and to adapt the changes. This investigation first introduces the motivation, the technical background, and then the objective of the study. A decision support instrument (i.e. schedule optimizer for mobile mining equipment) is proposed and described to address this issue. The method and related algorithms which are used in this instrument are presented and discussed. The proposed method was tested by using a real case of Kittilä mine located in Finland. The result suggests that the proposed method can considerably improve the working efficiency and reduce the working time of the underground mine.

Suggested Citation

  • Zhen Song & Håkan Schunnesson & Mikael Rinne & John Sturgul, 2015. "Intelligent Scheduling for Underground Mobile Mining Equipment," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-21, June.
    • Handle: RePEc:plo:pone00:0131003
    DOI: 10.1371/journal.pone.0131003
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    References listed on IDEAS

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    Cited by:

    1. Siyu Tu & Mingtao Jia & Liguan Wang & Shuzhao Feng & Shuang Huang, 2023. "A Dynamic Scheduling Model for Underground Metal Mines under Equipment Failure Conditions," Sustainability, MDPI, vol. 15(9), pages 1-18, April.
    2. Akshay Chowdu & Peter Nesbitt & Andrea Brickey & Alexandra M. Newman, 2022. "Operations Research in Underground Mine Planning: A Review," Interfaces, INFORMS, vol. 52(2), pages 109-132, March.
    3. Siyu Tu & Mingtao Jia & Liguan Wang & Shuzhao Feng & Shuang Huang, 2022. "A Multi-Equipment Task Assignment Model for the Horizontal Stripe Pre-Cut Mining Method," Sustainability, MDPI, vol. 14(24), pages 1-18, December.
    4. Jie Hou & Guoqing Li & Lianyun Chen & Hao Wang & Nailian Hu, 2022. "Optimization of Truck–Loader Matching Based on a Simulation Method for Underground Mines," Sustainability, MDPI, vol. 15(1), pages 1-14, December.
    5. Chimunhu, Prosper & Topal, Erkan & Ajak, Ajak Duany & Asad, Waqar, 2022. "A review of machine learning applications for underground mine planning and scheduling," Resources Policy, Elsevier, vol. 77(C).

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