Abstract
In the planning of any mining operation, the main objective is the maximization of the profits. However, at the moment of performing this task, a series of assumptions are made, which, in some cases, are far from reality, such as the estimation of the sale price and the ore concentrations inside the deposit. In the present paper, a stochastic optimization model based on genetic algorithms is proposed in conjunction with Monte Carlo simulation, where it is possible to include and measure the risk to which open-pit mining operations are exposed for a polymetallic deposit, having as main objective maximizing the net present value for a specific risk level. Among the main results of the investigation, it was determined that the optimal production scheduling does not result in the longer life of mine. This can be attributed to the accumulated uncertainty that occurs as the life of mine increases.
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Franco-Sepulveda, G., Jaramillo, G.P., Del Rio, J.C. (2019). Use of Genetic Algorithms for Optimization of Open-Pit Mining Operations with Geological and Market Uncertainty. In: Widzyk-Capehart, E., Hekmat, A., Singhal, R. (eds) Proceedings of the 27th International Symposium on Mine Planning and Equipment Selection - MPES 2018. Springer, Cham. https://doi.org/10.1007/978-3-319-99220-4_9
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DOI: https://doi.org/10.1007/978-3-319-99220-4_9
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