Abstract

Sophisticated models have progressively been developed to address the challenges related to long-term, open-pit mine planning under conditions of geological uncertainty. Prior research has acknowledged that strategies for mine planning and the design of mineral concentrators are interdependent; thus, it is highly desirable to optimize them together. However, achieving detailed holistic optimization of the entire mineral value chain remains unresolved because of the inherent limitations associated with mathematical formulations and computational processing capacity. This paper details a method that contributes to bridging these limitations by employing a novel parallelized variable neighborhood descent approach combined with an embedded mass-balance component using linear programming techniques refined through Dantzig-Wolfe decomposition. This approach is exemplified through a case study of a gold deposit, which illustrates the enhanced performance capabilities of the new algorithm. The findings demonstrate significant improvements in the optimization process for mine planning, providing a stronger link between the mine's output and processing plant's capabilities.