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Optimizing mining rates under financial uncertainty in global mining complexes

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  • Kizilkale, Arman C.
  • Dimitrakopoulos, Roussos

Abstract

This paper presents a distributed and dynamic programming framework to the mining production rate target tracking of multiple metal mines under financial uncertainty. A single mine׳s target tracking is stated as a stochastic optimization problem and the solution is obtained by solving the dynamic program which gives the optimal production rate schedule of each mine as a Markovian feedback control on the price process. The global solution is distributed on multiple mines by a policy iteration method, and this iterative method is shown to provide the unique equilibrium among Markovian strategies. Numerical results confirm the efficacy of the proposed global method when compared to individual optimization of mining rate target tracking.

Suggested Citation

  • Kizilkale, Arman C. & Dimitrakopoulos, Roussos, 2014. "Optimizing mining rates under financial uncertainty in global mining complexes," International Journal of Production Economics, Elsevier, vol. 158(C), pages 359-365.
    • Handle: RePEc:eee:proeco:v:158:y:2014:i:c:p:359-365
    DOI: 10.1016/j.ijpe.2014.08.009
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    References listed on IDEAS

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    1. M W A Asad & R Dimitrakopoulos, 2013. "Implementing a parametric maximum flow algorithm for optimal open pit mine design under uncertain supply and demand," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 64(2), pages 185-197, February.
    2. Evatt, Geoffrey William & Soltan, Mousa Omid & Johnson, Paul V., 2012. "Mineral reserves under price uncertainty," Resources Policy, Elsevier, vol. 37(3), pages 340-345.
    3. Schwartz, Eduardo S, 1997. "The Stochastic Behavior of Commodity Prices: Implications for Valuation and Hedging," Journal of Finance, American Finance Association, vol. 52(3), pages 923-973, July.
    4. 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. Rimélé, Adrien & Dimitrakopoulos, Roussos & Gamache, Michel, 2020. "A dynamic stochastic programming approach for open-pit mine planning with geological and commodity price uncertainty," Resources Policy, Elsevier, vol. 65(C).
    2. Armstrong, Margaret & Lagos, Tomas & Emery, Xavier & Homem-de-Mello, Tito & Lagos, Guido & Sauré, Denis, 2021. "Adaptive open-pit mining planning under geological uncertainty," Resources Policy, Elsevier, vol. 72(C).
    3. Nwaila, Glen T. & Frimmel, Hartwig E. & Zhang, Steven E. & Bourdeau, Julie E. & Tolmay, Leon C.K. & Durrheim, Raymond J. & Ghorbani, Yousef, 2022. "The minerals industry in the era of digital transition: An energy-efficient and environmentally conscious approach," Resources Policy, Elsevier, vol. 78(C).
    4. 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.
    5. Xu, Meng & Shang, Pengjian & Zhang, Sheng, 2021. "Multiscale Rényi cumulative residual distribution entropy: Reliability analysis of financial time series," Chaos, Solitons & Fractals, Elsevier, vol. 143(C).
    6. 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.

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