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Production scheduling optimisation for sublevel stoping mines using mathematical programming: A review of literature and future directions

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  • Sotoudeh, Farzad
  • Nehring, Micah
  • Kizil, Mehmet
  • Knights, Peter
  • Mousavi, Amin

Abstract

The mining industry continues to face the problem of depleting grades and the subsequent significant variation in economic, environmental and technological characteristics have been observed. This has raised the importance of the production scheduling process due to its significant role in the profitability and efficiency of any mining operation. Among underground mining methods, sublevel stoping are a commonly used method in large-scale mining. It is a versatile and productive method that is primarily used for steeply deeping orebodies with regular shape, defined ore boundaries and competent ore and host rock. The sequence and extraction of stopes is critical in maximizing profitability, which requires careful consideration over the mine life. This is mainly due to geotechnical considerations within stoping envelopes to achieve stable ground conditions through subsequent backfilling. This in turn significantly raises the complexity of the scheduling process. This paper reviews previous studies related to production scheduling optimisation of sublevel stope mines using mathematical programming and recommends suggestions for future works.

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  • Sotoudeh, Farzad & Nehring, Micah & Kizil, Mehmet & Knights, Peter & Mousavi, Amin, 2020. "Production scheduling optimisation for sublevel stoping mines using mathematical programming: A review of literature and future directions," Resources Policy, Elsevier, vol. 68(C).
    • Handle: RePEc:eee:jrpoli:v:68:y:2020:i:c:s030142072030235x
    DOI: 10.1016/j.resourpol.2020.101809
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    References listed on IDEAS

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    Cited by:

    1. Lu Chen & Qinghua Gu & Rui Wang & Zhidong Feng & Chao Zhang, 2022. "Comprehensive Utilization of Mineral Resources: Optimal Blending of Polymetallic Ore Using an Improved NSGA-III Algorithm," Sustainability, MDPI, vol. 14(17), pages 1-19, August.
    2. Furtado e Faria, Matheus & Dimitrakopoulos, Roussos & Lopes Pinto, Cláudio Lúcio, 2022. "Integrated stochastic optimization of stope design and long-term underground mine production scheduling," Resources Policy, Elsevier, vol. 78(C).
    3. 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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