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Underground mine scheduling under uncertainty

Author

Listed:
  • Nesbitt, Peter
  • Blake, Lewis R.
  • Lamas, Patricio
  • Goycoolea, Marcos
  • Pagnoncelli, Bernardo K.
  • Newman, Alexandra
  • Brickey, Andrea

Abstract

Underground mine schedules seek to determine start dates for activities related to the extraction of ore, often with an objective of maximizing net present value; constraints enforce geotechnical precedence between activities, and restrict resource consumption on a per-time-period basis, e.g., development footage and extracted tons. Strategic schedules address these start dates at a coarse level, whereas tactical schedules must account for the day-to-day variability of underground mine operations, such as unanticipated equipment breakdowns and ground conditions, both of which might slow production. At the time of this writing, the underground mine scheduling literature is dominated by a deterministic treatment of the problem, usually modeled as a Resource Constrained Project Scheduling Problem (RCPSP), which precludes mine operators from reacting to unforeseen circumstances. Therefore, we propose a stochastic integer programming framework that: (i) characterizes uncertainty in duration and economic value for each underground mining activity; (ii) formulates a new stochastic variant of the RCPSP; (iii) suggests an optimization-based heuristic; and, (iv) produces implementable, tactical schedules in a practical amount of time and provides corresponding managerial insights.

Suggested Citation

  • Nesbitt, Peter & Blake, Lewis R. & Lamas, Patricio & Goycoolea, Marcos & Pagnoncelli, Bernardo K. & Newman, Alexandra & Brickey, Andrea, 2021. "Underground mine scheduling under uncertainty," European Journal of Operational Research, Elsevier, vol. 294(1), pages 340-352.
  • Handle: RePEc:eee:ejores:v:294:y:2021:i:1:p:340-352
    DOI: 10.1016/j.ejor.2021.01.011
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    References listed on IDEAS

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    Cited by:

    1. Zeng, Lanyan & Liu, Shi Qiang & Kozan, Erhan & Corry, Paul & Masoud, Mahmoud, 2021. "A comprehensive interdisciplinary review of mine supply chain management," Resources Policy, Elsevier, vol. 74(C).
    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. Yifei Zhao & Jianhong Chen & Shan Yang & Yi Chen, 2022. "Mining Plan Optimization of Multi-Metal Underground Mine Based on Adaptive Hybrid Mutation PSO Algorithm," Mathematics, MDPI, vol. 10(14), pages 1-20, July.
    4. 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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