Improving Confidence in Evolutionary Mine Scheduling via Uncertainty Discounting

TL;DR

This paper presents a novel method that incorporates uncertainty into mine scheduling by discounting profits within an evolutionary algorithm, providing probabilistic bounds on outcomes to improve decision-making under geological uncertainty.

Contribution

It introduces an innovative approach that integrates uncertainty discounting into evolutionary mine planning algorithms, offering probabilistic profit bounds and better risk management.

Findings

Effective use of uncertainty information in mine planning.

Improved downside risk management in scheduling.

Successful experimental validation with industry software.

Abstract

Mine planning is a complex task that involves many uncertainties. During early stage feasibility, available mineral resources can only be estimated based on limited sampling of ore grades from sparse drilling, leading to large uncertainty in under-sampled parts of the deposit. Planning the extraction schedule of ore over the life of a mine is crucial for its economic viability. We introduce a new approach for determining an "optimal schedule under uncertainty" that provides probabilistic bounds on the profits obtained in each period. This treatment of uncertainty within an economic framework reduces previously difficult-to-use models of variability into actionable insights. The new method discounts profits based on uncertainty within an evolutionary algorithm, sacrificing economic optimality of a single geological model for improving the downside risk over an ensemble of equally likely models. We provide experimental studies using Maptek's mine planning software Evolution. Our results show that our new approach is successful for effectively making use of uncertainty information in the mine planning process.