Abstract
Long-term mine scheduling decisions are made under uncertainty due to the sparse nature of the exploration data. Deviations from the planned mine/mill production targets can lead to financial losses. Incorporating the geological uncertainty into different aspects of planning, including orebody layout determination and mine scheduling has the potential to quantify and mitigate the risk of not meeting production targets. By doing so, mine schedules become more robust and less sensitive to geological uncertainty. In open-pit operations, uncertainty handling approaches such as stochastic integer programming, diverse meta-heuristics and heuristics have been conducted and examined in the literature. However, in underground mining, uncertainty management has been addressed less frequently due to the complex geotechnical requirements involved in these mining methods. Since mining methods, such as sublevel stoping, have high development and operational costs, it is important to account for uncertainty in stope sequencing to build a resilient schedule. In this study, genetic algorithms are used to address the issue of geological uncertainty in the scheduling of stopes in underground mines using the sublevel stoping method. More specifically, a genetic algorithm based approach is developed to improve the robustness of the schedule by maximizing the NPV and minimizing deviations from production targets by simultaneously considering a number of equiprobable orebody realizations. Additionally, a similarity metric is going to be developed to assess the resemblance among schedules to further enhance the efficiency of the algorithm by allowing the comparison of schedules during the implementation of the algorithm.