Abstract
Short-term underground mine planning is a challenging optimization problem since it deals with many resources and activities conducted in a confined working space. Moreover, underground mining operations are subject to multiple uncertainties, such as the variation in activity durations. Conventional methods for mine planning do not consider uncertainty in scheduling decisions and thus result in impractical solutions. In this research, two different approaches are developed using Constraint Programming (CP) to create robust schedules. These approaches represent two ways to address the uncertainty of activity durations in the short-term underground mine scheduling problem. The first approach builds a robust schedule using multiple scenarios of the problem. The CP model enables to produce a set of ordered robust sequences of activities performed on available machines over multiple scenarios. In the second approach, a confidence constraint is proposed in the CP model to ensure sufficiently long durations for activities with a certain threshold probability. The confidence threshold value is defined based on risk tolerance in underground operations. Both models are tested on real-world data sets from a Canadian underground gold mine. A simulation model is designed to evaluate the robustness of schedules generated using different approaches. Results demonstrate that both scenario-based and confidence-constraint methods outperform the deterministic model by generating short-term schedules that are more robust to uncertainties in underground operations.