Modelling and optimization of Run-of-Mine stockpile recovery

Run-of-Mine stockpiles are essential components in the mining value chain because they can be used as temporary storage to balance inflow and outflow and provide an opportunity for blending material. Stockpile schedulers plan stockpile recovery to balance throughput and material specifications to deliver for the supply chain's next stage. There are technical limits on deliveries where "failure to meet"can lead to significant penalty fees, increased operational costs due to poor operational plans or over-delivery to material specifications. Currently, human experts determine the planning of stockpile recovery in practice. However, this approach is error prone due to the complex distribution of materials within a stockpile and the inability to foresee upcoming deliveries efficiently. In this paper, we model the stockpile recovery problem as a combinatorial optimization problem considering technical restrictions in real-world issues, and we investigate multiple scenarios and experiments. We apply deterministic and randomized greedy algorithms, as well as ant colony optimization algorithms integrated with local search. We compare all algorithms with a rule of thumb heuristic to evaluate our methodology's quality. Our findings show that ant colony optimization outperforms other algorithms, and the variant integrated with swap and insert local search operators finds the best solutions.