Short-term mine scheduling in open-pit mines consists of meeting the objectives defined by the long-term mine production schedule. Short-term scheduling in open-pit mines has multiple hierarchical objectives to optimize. This work proposes an optimization methodology to generate a short-term open-pit mine production schedule optimizing multiple hierarchical objectives. For the generation of mine schedules, we propose an optimization model based on mixed-integer linear programming. This model considers the usual restrictions of mine sequencing and also takes into account both time and cost of movement between phases of each shovel. In order to optimize the multiple hierarchical short-term objectives, we apply the hierarchical and weighted sum methods in the proposed optimization model. We verify this methodology in a real open-pit mine case study. The results show that both methods generate short-term mine schedules optimizing the different short-term objectives.

Snowfall can lead to the cessation of production in a truck-shovel mining system. A snow road removal procedure that is performed simultaneously with the production operation is introduced to avoid this situation. A discrete-event simulation model is built to evaluate both operations under different configurations. An operation policy is designed based on the simulations results. This policy defines the manner in which production and snow removal operations are conducted depending on the intensity of the snowfall. The operational policy demonstrates that production can be maintained at high snowfall intensities.

Uncertainty in short-term grade estimations can significantly affect destination policies and dig-limit definitions in open-pit mining. However, most dig-limit techniques still rely on deterministic methods and manual procedures. This study proposes a stochastic optimization model for the dig-limit definition problem that incorporates geological uncertainty through multiple grade scenarios and explicitly controls deviations from production targets. Two real case studies were evaluated to compare the stochastic formulation against deterministic and manual definitions. Results show that the stochastic model systematically improves economic performance, with profit increases of up to 2.3% over deterministic policies and up to 4.3% when compared against manual solutions. The stochastic solution also reduces deviations from metal and grade targets, producing more stable outcomes across scenarios. The model is computationally efficient, with solution times below 25 s for all case studies, which are compatible with practical short-term planning workflows. Overall, our findings demonstrate that incorporating grade variability into the dig-limit definition improves profitability and reliability in short-term mine planning horizons.