A critical review of bench aggregation and mining cut clustering techniques based on optimization and artificial intelligence to enhance the open-pit mine planning

Abstract

Determining the mining sequence is one of the main objectives in mine planning. However, depending on the size of the analyzed instances, such activity might become an extremely difficult task, despite current computational capacity. In addition, determining a feasible and operational mining sequence is also challenging, so practitioners usually employ strategies to segment and simplify the main problem, such as splitting it into distinct time horizons and aggregating blocks into clusters. This paper aims to perform a critical review about the different clustering methodologies and algorithms used for mining-block aggregation, with the purpose of understanding the proposed solutions and identifying the gaps found in the current literature. The reviewed aggregation strategies encompass the modelling of tabular deposits as sets of layers and grouping of blocks in benches, bench-phases, and mining cuts. Among the optimization techniques evaluated, one may find heuristics, artificial intelligence, and exact approaches, relying on deterministic or uncertainty-based methodologies, considering approximately six decades of studies and covering fifty-eight works published in journals and proceedings from 1967 to 2022. In addition to what is seen within the literature analyzed, we also propose future research directions, such as approaches and algorithms not yet implemented to solve the block aggregation problem, thus presenting opportunities for further research in this field.