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A Dynamic Scheduling Model for Underground Metal Mines under Equipment Failure Conditions

Author

Listed:
  • Siyu Tu

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

  • Mingtao Jia

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

  • Liguan Wang

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

  • Shuzhao Feng

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

  • Shuang Huang

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

Abstract

Equipment failure is a common problem in mining operations, resulting in significant delays and reductions in production efficiency. To address this problem, this paper proposes a dynamic scheduling model for underground metal mines under equipment failure conditions. The model aims to minimize the impact of equipment failures on production operations while avoiding extensive equipment changes. A case study of the southeastern mining area of the Chambishi Copper Mine is presented to demonstrate the effectiveness of the proposed model. The initial plan was generated using the multi-equipment task assignment model for the horizontal stripe pre-cut mining method. After equipment breakdown, the proposed model was used to reschedule the initial plan. Then, a comparative analysis was carried out. The results show that the proposed model effectively reduces the impact of equipment failures on production operations and improves overall mining execution at a low management cost. In general, the proposed model can assist schedulers in allocating equipment, coping with the disturbing effects of equipment failure, and improving mine production efficiency.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:9:p:7306-:d:1134662
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    References listed on IDEAS

    as
    1. Marco Schulze & Julia Rieck & Cinna Seifi & Jürgen Zimmermann, 2016. "Machine scheduling in underground mining: an application in the potash industry," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 38(2), pages 365-403, March.
    2. 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).
    3. 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.
    4. Prerita Odeyar & Derek B. Apel & Robert Hall & Brett Zon & Krzysztof Skrzypkowski, 2022. "A Review of Reliability and Fault Analysis Methods for Heavy Equipment and Their Components Used in Mining," Energies, MDPI, vol. 15(17), pages 1-27, August.
    Full references (including those not matched with items on IDEAS)

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