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A variable neighbourhood descent algorithm for the open-pit mine production scheduling problem with metal uncertainty

Author

Listed:
  • Amina Lamghari

    (McGill University, Quebec, Canada)

  • Roussos Dimitrakopoulos

    (McGill University, Quebec, Canada)

  • Jacques A Ferland

    (University of Montreal, Quebec, Canada)

Abstract

Uncertainty is an inherent aspect of the open-pit mine production scheduling problem (MPSP); however, little is reported in the literature about solution methods for the stochastic versions of the problem. In this paper, two variants of a variable neighbourhood descent algorithm are proposed for solving the MPSP with metal uncertainty. The proposed methods are tested and compared on actual large-scale instances, and very good solutions, with an average deviation of less than 3% from optimality, are obtained within a few minutes up to a few hours.

Suggested Citation

  • Amina Lamghari & Roussos Dimitrakopoulos & Jacques A Ferland, 2014. "A variable neighbourhood descent algorithm for the open-pit mine production scheduling problem with metal uncertainty," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 65(9), pages 1305-1314, September.
    • Handle: RePEc:pal:jorsoc:v:65:y:2014:i:9:p:1305-1314
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    Cited by:

    1. Lin, Jingsi & Asad, Mohammad Waqar Ali & Topal, Erkan & Chang, Ping & Huang, Jinxin & Lin, Wei, 2024. "A novel model for sustainable production scheduling of an open-pit mining complex considering waste encapsulation," Resources Policy, Elsevier, vol. 91(C).
    2. Samavati, Mehran & Essam, Daryl & Nehring, Micah & Sarker, Ruhul, 2017. "A methodology for the large-scale multi-period precedence-constrained knapsack problem: an application in the mining industry," International Journal of Production Economics, Elsevier, vol. 193(C), pages 12-20.
    3. Levinson, Zachary & Dimitrakopoulos, Roussos, 2023. "Connecting planning horizons in mining complexes with reinforcement learning and stochastic programming," Resources Policy, Elsevier, vol. 86(PB).
    4. Lamghari, Amina & Dimitrakopoulos, Roussos, 2016. "Network-flow based algorithms for scheduling production in multi-processor open-pit mines accounting for metal uncertainty," European Journal of Operational Research, Elsevier, vol. 250(1), pages 273-290.
    5. Noriega, Roberto & Pourrahimian, Yashar, 2022. "A systematic review of artificial intelligence and data-driven approaches in strategic open-pit mine planning," Resources Policy, Elsevier, vol. 77(C).

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