Cutoff Grade Optimization on Operative Decision Variables with Geological Uncertainty in an Underground Gold Mine: A Real Case Study

Diego Alejandro Toro Morales, Giovanni Franco Sepúlveda, Erick de la Barra () and Juan Camilo Del Río Cuervo
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Diego Alejandro Toro Morales: Facultad de Minas, Universidad Nacional de Colombia, Medellín 050041, Colombia
Giovanni Franco Sepúlveda: Facultad de Minas, Universidad Nacional de Colombia, Medellín 050041, Colombia
Erick de la Barra: School of Business, Universidad Católica del Norte, Coquimbo 1780000, Chile
Juan Camilo Del Río Cuervo: Amazon Web Services (AWS), Seattle, WA 98109, USA

Mathematics, 2024, vol. 12, issue 22, 1-16

Abstract: In mine planning, uncertainty assessment is essential to properly value a mining project. Geological, operational, and economical risks have to be considered to find the variable values that maximize the profit of the project. In this research, geological uncertainties are taken into account to assess the economic value of an underground gold mine. The scenarios considered are the tonnage-grade curves which are simulated by the Monte Carlo method. The decision variables are the cutoff grade (CoG), the Life of Mine (LoM), and operational variables, namely the mining and the processing capacity. In order to assess the economic value, we maximize the Net Present Value (NPV), which is carried out by a Genetic Algorithm (GA). This optimization, so-called implicit optimization, generates results of the probabilistic model which are compared with the deterministic one; the results found for a real underground gold mine show that, in the probabilistic case, the Net Present Value is higher and the time horizon is shorter than the results of the deterministic case, and the mining and the processing capacity are higher for the probabilistic case than the deterministic one.

Keywords: mine scheduling; optimization under uncertainty; Monte Carlo simulations (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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