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A heuristic approach for the stochastic optimization of mine production schedules

Author

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  • Luis Montiel

    (McGill University)

  • Roussos Dimitrakopoulos

    (McGill University)

Abstract

Traditionally, mining engineers plan an open pit mine considering pre-established conditions of operation of the plant(s) derived from a previous plant optimization. By contrast, mineral processing engineers optimize the processing plants by considering a regular feed from the mine, with respect to quantity and quality of the materials. The methods implemented to optimize mine and metallurgical plans simultaneously are known in the mining industry as global or simultaneous optimizers. The development of these methods has been of major concern for the mining industry over the last decade. Some algorithms are available in commercial mining software packages however, these algorithms ignore the inherent geological uncertainty associated with the deposit being considered, which leads to shortfalls in production, quality, and expected cashflows. This paper presents a heuristic method to generate life-of-mine production schedules that consider operating alternatives for processing plants and incorporate geological uncertainty. The method uses iterative improvement by swapping periods and destinations of the mining blocks to generate the final solution. The implementation of the method at a copper deposit shows its ability to control mine and processing capacities while increasing the expected net present value by 30% when compared with a solution generated using a standard industry practice.

Suggested Citation

  • Luis Montiel & Roussos Dimitrakopoulos, 2017. "A heuristic approach for the stochastic optimization of mine production schedules," Journal of Heuristics, Springer, vol. 23(5), pages 397-415, October.
  • Handle: RePEc:spr:joheur:v:23:y:2017:i:5:d:10.1007_s10732-017-9349-6
    DOI: 10.1007/s10732-017-9349-6
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    References listed on IDEAS

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    1. Lamghari, Amina & Dimitrakopoulos, Roussos, 2012. "A diversified Tabu search approach for the open-pit mine production scheduling problem with metal uncertainty," European Journal of Operational Research, Elsevier, vol. 222(3), pages 642-652.
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    Cited by:

    1. 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).
    2. Friedrich-Wilhelm Wellmer & Roland W. Scholz, 2018. "What Is the Optimal and Sustainable Lifetime of a Mine?," Sustainability, MDPI, vol. 10(2), pages 1-22, February.
    3. Devendra Joshi & Marwan Ali Albahar & Premkumar Chithaluru & Aman Singh & Arvind Yadav & Yini Miro, 2022. "A Novel Approach to Integrating Uncertainty into a Push Re-Label Network Flow Algorithm for Pit Optimization," Mathematics, MDPI, vol. 10(24), pages 1-20, December.
    4. Ashish Kumar & Roussos Dimitrakopoulos & Marco Maulen, 2020. "Adaptive self-learning mechanisms for updating short-term production decisions in an industrial mining complex," Journal of Intelligent Manufacturing, Springer, vol. 31(7), pages 1795-1811, October.
    5. Del Castillo, M. Fernanda & Dimitrakopoulos, Roussos, 2019. "Dynamically optimizing the strategic plan of mining complexes under supply uncertainty," Resources Policy, Elsevier, vol. 60(C), pages 83-93.
    6. Gilani, Seyyed-Omid & Sattarvand, Javad & Hajihassani, Mohsen & Abdullah, Shahrum Shah, 2020. "A stochastic particle swarm based model for long term production planning of open pit mines considering the geological uncertainty," Resources Policy, Elsevier, vol. 68(C).
    7. 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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