  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.
