Applying artificial intelligence for optimal production scheduling and phase design in open pit mining
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Authors: Loor, V; Morales, N Open access courtesy of:
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This paper is hosted with the kind permission of the Universidad de Chile, Eighth International Conference & Exhibition on Mass Mining, 2020.
DOI https://doi.org/10.36487/ACG_repo/2063_111
Cite As:
Loor, V & Morales, N 2020, 'Applying artificial intelligence for optimal production scheduling and phase design in open pit mining', in R Castro, F Báez & K Suzuki (eds), MassMin 2020: Proceedings of the Eighth International Conference & Exhibition on Mass Mining, University of Chile, Santiago, pp. 1451-1466, https://doi.org/10.36487/ACG_repo/2063_111
Abstract:
The open-pit mine production scheduling (OPPS) problem aims to determine the extraction sequence of mining blocks of an orebody. The OPPS presents several restrictions that create a combinatorial optimization problem classified as NP-hard. Generally, an optimal solution for OPPS cannot be obtained in an acceptable computation time using linear programming; therefore, approximation methods called heuristics have been used to solve it. In this paper, an artificial intelligence (AI) based methodology is proposed to obtain operative pushbacks in open-pit mines respecting operational and design constraints. This integrated approach is achieved through a Genetic Algorithm and a clustering algorithm (k-means). A Genetic Algorithm is a search heuristic inspired by Charles Darwin’s theory of natural evolution and is used to solve NP-hard problems. This methodology has been tested in an iron mine and a gold mine and has been shown to be a practical, viable approach. Results show that pushbacks obtained respect the design and operational constraints of pit extraction, while also maximizing the net present value (NPV).
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