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Connecting planning horizons in mining complexes with reinforcement learning and stochastic programming

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  • Levinson, Zachary
  • Dimitrakopoulos, Roussos

Abstract

Connecting short- and long-term production schedules in mining complexes is essential to ensure that the long-term production schedule is achievable at shorter timescales. Previous research that addresses optimizing mining complexes under uncertainty focus on simultaneously optimizing different components in the mining complex to capitalize on advantageous synergies. Typically, short- and long-term production schedules are optimized separately in a number of stages. This poses risk of schedule misalignment, which can adversely affect the economic outcome of a mining complex and the ability to meet long-term production forecasts at shorter timescales. A framework is proposed to jointly optimize short- and long-term production schedules by connecting planning horizons with stochastic mathematical programming and reinforcement learning. The solution approach is tested in a large operating copper mining complex and demonstrates significant improvements in the resulting production and financial forecasts.

Suggested Citation

  • Levinson, Zachary & Dimitrakopoulos, Roussos, 2023. "Connecting planning horizons in mining complexes with reinforcement learning and stochastic programming," Resources Policy, Elsevier, vol. 86(PB).
    • Handle: RePEc:eee:jrpoli:v:86:y:2023:i:pb:s0301420723008474
    DOI: 10.1016/j.resourpol.2023.104136
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. Paithankar, Amol & Chatterjee, Snehamoy & Goodfellow, Ryan & Asad, Mohammad Waqar Ali, 2020. "Simultaneous stochastic optimization of production sequence and dynamic cut-off grades in an open pit mining operation," Resources Policy, Elsevier, vol. 66(C).
    4. Montiel, Luis & Dimitrakopoulos, Roussos, 2015. "Optimizing mining complexes with multiple processing and transportation alternatives: An uncertainty-based approach," European Journal of Operational Research, Elsevier, vol. 247(1), pages 166-178.
    5. 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.
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