Advanced mine optimisation under uncertainty using evolution
Date
2021
Authors
Reid, W.
Neumann, A.
Ratcliffe, S.
Neumann, F.
Editors
Krawiec, K.
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Conference paper
Citation
Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO '21), 2021 / Krawiec, K. (ed./s), pp.1605-1613
Statement of Responsibility
William Reid, Aneta Neumann, Simon Ratcliffe, Frank Neumann
Conference Name
Genetic and Evolutionary Computation Conference (GECCO) (10 Jul 2021 - 14 Jul 2021 : virtual online)
Abstract
In this paper, we investigate the impact of uncertainty in advanced mine optimisation. We consider Maptek’s software system Evolution which optimizes extraction sequences based on evolutionary computation techniques and quantify the uncertainty of the obtained solutions with respect to the ore deposit based on predictions obtained by ensembles of neural networks. Furthermore, we investigate the impact of staging on the obtained optimized solutions and discuss a wide range of components for this large scale stochastic optimisation problem which allow us to mitigate the uncertainty in the deposit while maintaining high profitability.
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© 2021 Copyright held by the owner/author(s). Publication rights licensed to ACM.