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Optimization of Truck–Loader Matching Based on a Simulation Method for Underground Mines

Author

Listed:
  • Jie Hou

    (School of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing 100083, China
    Anhui Mine IOT and Security Monitoring Technology Key Laboratory, Anhui 230601, China)

  • Guoqing Li

    (School of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing 100083, China)

  • Lianyun Chen

    (School of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing 100083, China
    Shandong Gold Group Co., Ltd., Jinan 250013, China)

  • Hao Wang

    (Mine Big Data Research Institute, China Coal Research Institute, Beijing 100013, China)

  • Nailian Hu

    (School of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing 100083, China)

Abstract

The choice of transportation system has an important impact on the production efficiency and economic behavior of underground mines. Trackless vehicle transportation has gradually become the main method in underground mines because mining companies have realized that mining efficiency can be improved using advanced vehicle mechanization and automation in the mining process. The extracted ore is loaded onto trucks by loaders in situ, then the trucks drive to ore passes for unloading Trucks load and unload ore in a cyclical manner between stopes and ore passes. Numerous trucks drive in tunnels simultaneously to achieve production targets, and there are interactions and influences among trucks, such as blocking and queuing, due to limited underground space. To address this issue, a transportation route model was built, and the ore transportation process was divided into three parts, including ore loading, truck transportation, and ore unloading. The simulation method was applied to optimize the number of loaders and trucks under the constraints of stope production capacity, transportation route and capacity, and vehicle capacity, to achieve the optimal vehicle utilization rate and transportation capability. The Monte Carlo simulation method was utilized to take the uncertainties of the transportation parameters into account to improve the robustness of the simulation results. The model was verified using the case study of an underground gold mine located in Shandong Province, China, with the objective of accomplishing optimal truck–loader matching considering various stopes in a mining area.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:15:y:2022:i:1:p:216-:d:1012787
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    References listed on IDEAS

    as
    1. 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.
    2. Moradi Afrapoli, Ali & Tabesh, Mohammad & Askari-Nasab, Hooman, 2019. "A multiple objective transportation problem approach to dynamic truck dispatching in surface mines," European Journal of Operational Research, Elsevier, vol. 276(1), pages 331-342.
    Full references (including those not matched with items on IDEAS)

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