Abstract:This study aims to significantly reduce the complexity of Mixed Integer Linear Programming (MILP) mathematical models in mining applications by using a hierarchical clustering algorithm for pre-processing block data, thereby solving complex problems in mining scheduling optimization. The essence of this method lies in the aggregation of numerous small blocks into relatively fewer and larger consolidated units based on the similarity of geological and mining-related properties of the blocks. This approach simplifies the variables and constraints of the model, reducing the computational resources and time required to solve optimization problems. An application case study of a large open-pit gold mining complex demonstrates the effectiveness of the hierarchical clustering algorithm in practice. The original MILP model, due to the immense number of blocks and high computational complexity, was not directly solvable. After applying the clustering algorithm, the number of blocks was sensibly reduced from 36183 to 5810 consolidated units, significantly reducing the scale of the problem.
