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.

翻译：矿山规划是一项涉及诸多不确定性的复杂任务。在早期可行性研究阶段，矿产资源量仅能基于稀疏钻孔中矿石品位的有限采样进行估算，导致矿床中采样不足区域存在巨大不确定性。在整个矿山生命周期中规划矿石开采顺序对其经济可行性至关重要。我们提出了一种新方法，用于确定"不确定性条件下的最优调度方案"，该方法可为每个时期的利润提供概率界限。这种在经济学框架内处理不确定性的方式，将先前难以使用的变异性模型转化为可操作的见解。该方法在进化算法中根据不确定性对利润进行折现，通过牺牲单一地质模型的经济最优性，来改善由多个等概率模型构成的集合中的下行风险。我们利用Maptek公司的矿山规划软件Evolution进行了实验研究。结果表明，我们的新方法能在矿山规划过程中有效利用不确定性信息。