Long-term open-pit mine planning is a critical stage of a mining project that seeks to establish the best strategy for extracting mineral resources, based on the assumption of several economic, geological and operational parameters. Conventionally, during this process it is common to use deterministic resource models to estimate in situ ore grades and to assume average values for geometallurgical variables. These assumptions cause risks that may negatively impact on the planned production and finally on the project value. This paper addresses the long-term planning of an open-pit mine considering (i) the incorporation of geometallurgical models given by equiprobable scenarios that allow for the assessing of the spatial variability and the uncertainty of the mineral deposit, and (ii) the use of stochastic integer programming model for risk analysis in direct block scheduling, considering the scenarios simultaneously. The methodology comprises two stages: pit optimization to generate initial ultimate pit limit per scenario and then to define a single ultimate pit based on reliability, and stochastic life-of-mine production scheduling to define block extraction sequences within the reliability ultimate pit to maximize the expected discounted value and minimize the total cost of production objective deviations. To evaluate the effect of the geometallurgical information, both stages consider different optimization strategies that depend on the economic model to be used and the type of processing constraints established in the scheduling. The results show that geometallurgical data with their associated uncertainties can change the decisions regarding pit limits and production schedule and, consequently, to impact the financial outcomes.

Artículo de publicación ISIIn order to establish a production plan, an open-pit mine is partitioned into a three-dimensional array of blocks. The order in which blocks are extracted and processed has a dramatic impact on the economic value of the exploitation. Since realistic models have millions of blocks and constraints, the combinatorial optimization problem of finding the extraction sequence that maximizes the profit is computationally intractable. In this work, we present a procedure, based on innovative aggregation and disaggregation heuristics, that allows us to get feasible and nearly optimal solutions. The method was tested on the public reference library. MineLib and improved the best known results in the literature in 9 of the 11 instances of the library. Moreover, the overall procedure is very scalable, which makes it a promising tool for large size problems. (C) 2015 Elsevier B.V. and Association of European Operational Research Societies (EURO) within the International Federation of Operational Research Societies (IFORS)FONDECYT 1140829; Basal Project CMM Universidad de Chile PFB03; Basal Project AMTC Universidad de Chile FB0809; Millenium Nucleus ICM/FIC RC130003; Anillo Project ACT-1106; ECOS Grant C13E03; STIC AmSud Project OVIMINE; MATH Amsud Project 15MATH-0

Production planning decisions in the mining industry are affected by geological, geometallurgical, economic and operational information. However, the traditional approach to address this problem often relies on simplified models that ignore the variability and uncertainty of these parameters. In this paper, two main sources of uncertainty are combined to obtain multiple simulated block models in an iron ore deposit that include the rock type and seven quantitative variables (grades of Fe, SiO2, S, P and K, magnetic ratio and specific gravity). To assess the effect of integrating these two sources of uncertainty in mine planning decision, stochastic and deterministic production scheduling models are applied based on the simulated block models. The results show the capacity of the stochastic mine planning model to identify and minimize risks, obtaining valuable information in ore content or quality at early stages of the project, and improving decision-making with respect to the deterministic production scheduling. Numerically speaking, the stochastic mine planning model improves 6% expected cumulative discounted cash flow and generates 16% more iron ore than deterministic model.

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