The short-term scheduling of activities in underground mines is an important step in mining operations. This procedure is a challenging optimization problem since it deals with many resources and activities conducted in a confined working space. Moreover, underground mining operations deal with multiple uncertainties such as the variation of activity durations. In this paper, a Constraint Programming (CP) model is proposed for short-term planning in underground mines. The developed model takes into account the technical requirements of underground operations to build realistic mine schedules. Furthermore, two different approaches are proposed based on the CP model for robust short-term underground mine scheduling. The first approach aims to create a robust schedule using multiple scenarios of the problem. This stochastic CP model enables to find a set of ordered robust sequences of activities performed by each available disjunctive resource over several scenarios. In the second approach, a confidence constraint is introduced in the CP model to specify the probability that the schedule generated won’t underestimate the duration of activities. The model allows the mine planner to control the risk level with which an optimized solution should be produced such that it can be implemented given the actual activity durations. The presented approaches are tested on real data sets of an underground gold mine in Canada. An evaluation model is designed to evaluate the robust performance of the proposed models. The experiments demonstrate that both scenario-based and confidence-constraint approaches outperform the deterministic model by generating schedules that are more robust to uncertainties in underground operations.