Publicado

2018-01-01

Direct stockpile scheduling: Mathematical formulation

Programación directa de pilas de acopio: Formulación matemática

DOI:

https://doi.org/10.15446/dyna.v85n204.62642

Palabras clave:

stockpile, goal programing, blending constraints, stockpile scheduling, bulk ore blending (en)
pilas de acopio, goal programing, restricciones de mezcla, agendamiento de pilas, mezcla de concetrado (es)

Autores/as

In a mining context, production scheduling’s main objective is to determine the best mining sequence of blocks to achieve the largest net present value and to maximize ore reserve exploitation. Stockpiling and blending procedures may represent very helpful alternatives for mine planning to ensure the ore quality and amount required by the processing plant. In order to satisfy industrial requirements of grades and tones, reducing stockpile fluctuations may represent a very important tool especially for medium and short term mine planning. Classical linear programing has been widely used to model blending problems at the mining industry, however this formulation allows only one objective formulation. The current work describes a system based on goal programing able to reach blending constraints desired by short/medium term planning. The proposed formulation achieves the best schedule scenario, ensuring cost constrains are respected. Hence, this study aims to provide support for both short and long term mine planning.
Según el contexto de industria minera la programación de producción es la mejor metodología para determinar la mejor secuencia de explotación y asi obtener el mejor valor presente líquido y explotar la reserva máxima. La construcción de pilas de acopio y de mezcla representa una alternativa valiosa para la planificación de minado permitiendo garantizar la calidad del concentrado y las especificaciones de la planta de tratamiento. Para satisfacer las especificaciones de leyes y tonelaje, y reducir las variaciones las pilas de acopio y mezcla cumplen un papel importante para la planificación de corto y largo plazo. La programación linear clásica viene siendo ampliamente utilizada en problemas de mezcla presentes en la industria minera entretanto esta formulación permite apenas trabajar con una sola función objetivo en su formulación. El presente trabajo describe un sistema basado en goal programing, capaz de alcanzar las restricciones requeridas en la planificación a corto y largo plazo de forma simultánea. La formulación propuesta obtiene el mejor escenario operacional garantizando que las restricciones de costos sean respetadas. Esta formulación es útil pues da soporte a la toma de decisiones en las actividades de planificación a largo y corto plazo.

Referencias

Chanda, E. and Dagdelen, K., Optimal blending of mine production using goal programming and interactive graphics systems. International Journal of Surface Milling. Reclamation and Environment, 9, pp. 203-208, 1995. DOI: 10.1080/09208119508964748

Chun-yue, S. (et all), Modeling and scheduling optimization for bulk ore blending process. Journal of Iron and Steel Research Inernational, 19, pp. 20-28, 2012. DOI: 10.1016/S1006-706X(13)60004-7

CPRM., Geoparque Quadrilátero Ferrífero (MG). Itabira: CPRM Press,1, PP. 183-220, 2014.

Dantzig, G.B., and Thapa, N.M., Linear Programming. New York: Springer-Verlag Press, 1997.

Giokas, D., The use of goal programming and data envelopment analysis for estimating efficient marginal costs of outputs. The Journal of the Operation Research Society, 48, pp. 319-323, 1997. DOI: 10.1057/palgrave.jors.2600376

Glover, F.E., Handbook of Metaheuristics. New York, Boston, Dordrecht, London, Moscow: Kluwer Academic Publishers, 2003.

Ignizio, J.P., A review of goal programming: a tool for multi objective analysis. The Journal of the Operation Research Society, 29, pp. 1109-1119, 1978. DOI: 10.1057/jors.1978.243

Johnson, B.T., Optimum open pit mine production scheduling, MSc Thesis, University of California, California, Berkley, 1968.

Killough, L.N. and Souders, T.L., A goal programming model for public accounting firms. The Accounting Review, [online]. 48, pp. 268-279, 1973. Available at http://www.jstor.org/stable/244917

Jones, D. and Tamiz, M., Practical Goal Programming, New York: Springer Books, 2010.

Leite, A.D., Production scheduling under metal uncertainty – Application of stochastic mathematical programming at an open pit copper mine and comparison to conventional scheduling. The Australasian Institute of Mining and Metallurgy, Spectrum Series, 17. Mining Technology, [online]. 116, pp. 109-118, 2010. Available at http://cgm.cs.mcgill.ca/~avis/courses/567/roussos/SR_RD.pdf

Montiel, L. and Dimitrakopoulos, R., Optimizing mining complexes with multiple processing and transportation alternatives: An uncertainty-based approach. European Journal of Operational Research, 247, pp. 166-178, 2015. DOI: 10.1016/j.ejor.2015.05.002

Moraes, E.F., et al., Um modelo de programação matemática para otimizar a composição de lotes deminério de ferro da mina Cauê da CVRD. REM: R. Esc. Minas, Ouro Preto, 59(3), pp. 299-306, 2006. DOI: 10.1590/S0370-44672006000300008

Rendu, J.-M., An Introduction to cut-off grade estimation. littleton: Society for Mining, Metallurgy, and Exploration, Inc. (SME). 2008.

Romero, C., Handbook of critical issues in goal programming. New York: Pergamon Press, ISBN -0-08-040661, 1991.

SME, SME mining engineering handbook – Englewood: 3rd ed. Society for Mining, Metallurgy, and Exploration, 2001.

Taylor, H.K., Rates of working of mines-a simple rule of thumb. Trans. Instn. Min Metall. (Sec.A: Min. industry), October, pp. A203–A204, 1986

Tulcanaza, E., Evaluación de recursos y negocios mineros. Oficina dos Textos, 1ª Ed. 2015.

Wells, H.M., Optimization of mining engineering design in mineral valuation. Mining Engineering December, [online]. pp. 1676-1684. 1978. Available at http://www.onemine.org/document/document. cfm?docid=7540

Ya-lin, W., et al., Multi-objective intelligent coordinating optimization blending system based on qualitative and quantitative synthetic model. Journal Cent. South University Technology, 13(13), pp. 552-557, 2006. DOI: 10.1007/s11771-006-0086-5

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Cómo citar

[1]
“Programación directa de pilas de acopio: Formulación matemática”, DYNA, vol. 85, no. 204, pp. 296–301, Jan. 2018, doi: 10.15446/dyna.v85n204.62642.