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A simulated annealing approach to mine production scheduling

Author

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  • M Kumral

    (Inonu University)

  • P A Dowd

    (University of Leeds)

Abstract

Increasing global competition, quality standards, environmental awareness and decreasing ore prices impose new challenges to mineral industries. Therefore, the extraction of mineral resources requires careful design and scheduling. In this research, simulated annealing (SA) is recommended to solve a mine production scheduling problem. First of all, in situ mineral characteristics of a deposit are simulated by sequential Gaussian simulation, and averaging the simulated characteristics within specified block volumes creates a three-dimensional block model. This model is used to determine optimal pit limits. A linear programming (LP) scheme is used to identify all blocks that can be included in the blend without violating the content requirements. The Lerchs–Grosmann algorithm using the blocks identified by the LP program determines optimal pit limits. All blocks that lie outside of the optimal pit limit are removed from the system and the blocks within the optimal pit are submitted to the production scheduling algorithm. Production scheduling optimization is carried out in two stages: Lagrangean parameterization, resulting in an initial sub-optimal solution, and multi-objective SA, improving the sub-optimal schedule further. The approach is demonstrated on a Western Australian iron ore body.

Suggested Citation

  • M Kumral & P A Dowd, 2005. "A simulated annealing approach to mine production scheduling," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(8), pages 922-930, August.
    • Handle: RePEc:pal:jorsoc:v:56:y:2005:i:8:d:10.1057_palgrave.jors.2601902
    DOI: 10.1057/palgrave.jors.2601902
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    References listed on IDEAS

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    1. Dowsland, Kathryn A., 1993. "Some experiments with simulated annealing techniques for packing problems," European Journal of Operational Research, Elsevier, vol. 68(3), pages 389-399, August.
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    Cited by:

    1. Rafael Epstein & Marcel Goic & Andrés Weintraub & Jaime Catalán & Pablo Santibáñez & Rodolfo Urrutia & Raúl Cancino & Sergio Gaete & Augusto Aguayo & Felipe Caro, 2012. "Optimizing Long-Term Production Plans in Underground and Open-Pit Copper Mines," Operations Research, INFORMS, vol. 60(1), pages 4-17, February.
    2. Alipour, Aref & Khodaiari, Ali Asghar & Jafari, Ahmad & Tavakkoli-Moghaddam, Reza, 2022. "An integrated approach to open-pit mines production scheduling," Resources Policy, Elsevier, vol. 75(C).
    3. J Jackman & Z Guerra de Castillo & S Olafsson, 2011. "Stochastic flow shop scheduling model for the Panama Canal," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(1), pages 69-80, January.
    4. Danish, Abid Ali Khan & Khan, Asif & Muhammad, Khan & Ahmad, Waqas & Salman, Saad, 2021. "A simulated annealing based approach for open pit mine production scheduling with stockpiling option," Resources Policy, Elsevier, vol. 71(C).
    5. Shishvan, Masoud Soleymani & Sattarvand, Javad, 2015. "Long term production planning of open pit mines by ant colony optimization," European Journal of Operational Research, Elsevier, vol. 240(3), pages 825-836.
    6. Christina N. Burt & Lou Caccetta, 2014. "Equipment Selection for Surface Mining: A Review," Interfaces, INFORMS, vol. 44(2), pages 143-162, April.
    7. 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).
    8. 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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