Advanced Ore Mine Optimisation under Uncertainty Using Evolution

TL;DR

This paper explores advanced ore mine optimization under uncertainty using evolutionary algorithms, neural network ensembles, and staging strategies to improve profitability and manage deposit uncertainties.

Contribution

It introduces a comprehensive stochastic optimization framework combining evolutionary computation and neural network ensembles for ore mine planning.

Findings

Neural network ensembles effectively quantify deposit uncertainty.

Staging strategies improve optimization robustness.

The approach maintains high profitability despite uncertainties.

Abstract

In this paper, we investigate the impact of uncertainty in advanced ore 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 to mitigate the uncertainty in the ore deposit while maintaining high profitability.