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Dynamically optimizing the strategic plan of mining complexes under supply uncertainty

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  • Del Castillo, M. Fernanda
  • Dimitrakopoulos, Roussos

Abstract

Mining complexes are comprised of multiple mines and mineral processing streams, each governed by internal (mineral deposit, operation) and external (commodity prices) uncertainties, and must be optimized jointly to manage technical risk and maximize economic value. This study presents a method that optimizes annual production scheduling of an open pit mining complex by developing a solution that provides a unique strategic mine plan that integrates feasible alternatives over investment decisions along the life of the asset. Accordingly, the long-term optimization is presented as a dynamic plan, which allows planning upfront for possible configuration transitions due to new capital investments, facilitating change. This method uses an adapted multistage stochastic programming model which expands upon the two-stage framework by performing multiple recourse stages that are solved iteratively, allowing feasible mine designs in a scenario-tree structure. In this model, dynamic investment decisions are made sequentially over the mine production schedule of related mines, based on new information that becomes available in each time period; these decision variables activate costs and effects over the model, letting the optimizer choose the capital investments to be considered at the mining and/or processing components of the mining complex. A copper open pit mining complex is used to test the proposed model, with options to invest in the truck and shovel fleet, and a secondary crusher to increase related capacities. Results show a substantial probability that the mine design should branch, presenting an increased expected net present value of over US$170M compared to the two-stage stochastic formulation.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:jrpoli:v:60:y:2019:i:c:p:83-93
    DOI: 10.1016/j.resourpol.2018.11.019
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    References listed on IDEAS

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    9. Adrien Rimélé, M. & Dimitrakopoulos, Roussos & Gamache, Michel, 2018. "A stochastic optimization method with in-pit waste and tailings disposal for open pit life-of-mine production planning," Resources Policy, Elsevier, vol. 57(C), pages 112-121.
    10. Zhang, Jian & Dimitrakopoulos, Roussos G., 2017. "A dynamic-material-value-based decomposition method for optimizing a mineral value chain with uncertainty," European Journal of Operational Research, Elsevier, vol. 258(2), pages 617-625.
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    Cited by:

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    4. Nelis, Gonzalo & Morales, Nelson & Jelvez, Enrique, 2023. "Optimal mining cut definition and short-term open pit production scheduling under geological uncertainty," Resources Policy, Elsevier, vol. 81(C).
    5. Das, Ranajit & Topal, Erkan & Mardaneh, Elham, 2024. "Concurrent optimisation of open pit ore and waste movement with optimal haul road selection," Resources Policy, Elsevier, vol. 91(C).
    6. Madziwa, Lawrence & Pillalamarry, Mallikarjun & Chatterjee, Snehamoy, 2023. "Integrating stochastic mine planning model with ARDL commodity price forecasting," Resources Policy, Elsevier, vol. 85(PB).
    7. LaRoche-Boisvert, Mélanie & Dimitrakopoulos, Roussos & Ferland, Jacques A., 2021. "Simultaneous production scheduling and transportation optimization from mines to port under uncertain material supply," Resources Policy, Elsevier, vol. 73(C).
    8. Mehmanpazir, Farhad & Khalili-Damghani, Kaveh & Hafezalkotob, Ashkan, 2022. "Dynamic strategic planning: A hybrid approach based on logarithmic regression, system dynamics, Game Theory and Fuzzy Inference System (Case study Steel Industry)," Resources Policy, Elsevier, vol. 77(C).

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