A new mathematical model for the optimization of block aggregation in open pit mines

The open-pit production planning is one of the most important steps of mine design which becomes a hard and challenging optimization problem in large-scale mineral deposits. A common approach in such a situation is to cluster mining blocks (smallest mining units) into larger units. In this paper, an integer non-linear programming model of the constrained block clustering is developed with the objective of minimizing grade deviations while blocks are geometrically connected within a cluster and the shape and size of individual clusters are in the pre-defined range. Then, a population-based iterated local search algorithm is presented to solve this nonlinear model and find a near-optimum solution. The proposed model and the solution approach were applied to a case study of a gold and silver deposit with 40,947 blocks. The mining blocks are grouped into 1966 clusters which then mine planner can solve production scheduling in less computational time.