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Incorporating geo-metallurgical information into mine production scheduling

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  • M Kumral

    (Inonu University, School of Engineering)

Abstract

Economic characterization of mining parcels depends upon geo-metallurgical properties, which vary throughout orebody. Mine production scheduling should aim to obtain maximum utility from orebody in such a way as to ensure mine–mill reconciliation. As heterogeneity of geo-metallurgical variables increases, the scheduling will be a very complicated task. Geo-metallurgical and financial data used in the mine production scheduling are based on simulation and/or estimation generated from sparse drilling and unknown future events. Therefore, the scheduling process involves a significant degree of uncertainty. In order to deal with the uncertainty stemmed from geo-metallurgical and financial variables, two approaches are recommended in this paper. Firstly, mine production scheduling is formulated as a problem of stochastic programming with recourse. The extraction periods of mining blocks are treated as the first-stage variables and the block destinations represents a recourse vector. It is observed that the solution is implicitly robust. Secondly, the scheduling is expressed as a maximin problem to extract more uniform metal quantity in periods to coincide with mill requirements instead of maximization of net present value because the blending constraint in the traditional approach forces more uniform production. In the case where there is correlation between grade and geo-metallurgical variables, this model generates reasonably good results.

Suggested Citation

  • M Kumral, 2011. "Incorporating geo-metallurgical information into mine production scheduling," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(1), pages 60-68, January.
    • Handle: RePEc:pal:jorsoc:v:62:y:2011:i:1:d:10.1057_jors.2009.174
    DOI: 10.1057/jors.2009.174
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    References listed on IDEAS

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    1. Suvrajeet Sen & Julia L. Higle, 1999. "An Introductory Tutorial on Stochastic Linear Programming Models," Interfaces, INFORMS, vol. 29(2), pages 33-61, April.
    2. Seymour Kaplan, 1974. "Application of Programs with Maximin Objective Functions to Problems of Optimal Resource Allocation," Operations Research, INFORMS, vol. 22(4), pages 802-807, August.
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    Cited by:

    1. Gaurav Singh & Rodolfo García-Flores & Andreas Ernst & Palitha Welgama & Meimei Zhang & Kerry Munday, 2014. "Medium-Term Rail Scheduling for an Iron Ore Mining Company," Interfaces, INFORMS, vol. 44(2), pages 222-240, April.
    2. Aldin Ardian & Mustafa Kumral, 2021. "Enhancing mine risk assessment through more accurate reproduction of correlations and interactions between uncertain variables," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 34(3), pages 411-425, October.

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