The open-pit mine production scheduling problem aims to optimize the net present value of a mining asset. Several solution methods have been proposed to find the most profitable mining sequence. Such methods entail determining which mining blocks from those used to represent the related mineral deposit should be extracted and when. However, little is reported in the technical literature that considers the material flow once mined and, more specifically, incorporating stockpiling as part of the mine scheduling strategy, thus adding technical intricacies due to the difficulty of correctly modeling the materials' blending once sent into a stockpile.

In this paper, a new model is provided to address the topic of open-pit mine production scheduling considering multiple destinations for the mined material, including stockpiles, and accounting for multi-element uncertainty. Unlike conventional models, the proposed model allows for an accurate estimation of the resulting grade of the stockpile without using unrealistic assumptions or non-linear constraints. A solution approach based on extending the Bienstock and Zuckerberg algorithm to the stochastic optimization and two heuristics is presented and applied to different real-size instances. Results show that this approach provides a feasible integer solution within less than 1.7% of optimality in a reasonable time. Properties and limitations of the model presented are also discussed, and recommendations for further research are made.