A parametric block clustering technique for production scheduling of open pit mines under grade uncertainty

Long-term production scheduling for open-pit mines specifies yearly extraction sequences that maximise net present value while meeting various physical and operational requirements. However, solving long-term production scheduling problem using mixed integer programming models is computationally demanding, particularly for large block models considering grade uncertainty. This work introduces a parametric block clustering technique for aggregating blocks into positive-value clusters that meet the pit slope and cluster size requirements. Case studies using relatively large size block models indicate that the optimal solutions may lead to substantially reduced computational times, confirming the applicability of new block aggregation approach to real-world scenarios.