Multivariate stochastic mine scheduling targeting stationary grades considering risk uncertainty and hierarchical variables

In short-term mine planning for deposits characterized by multiple variables, sequential diglines are generated with an emphasis on maintaining consistent grade distributions across mining periods. This research integrates an array of mineral variables, including deleterious elements, to facilitate the establishment of excavation geometries that enable precise grade and volume control within operational production zones. Through the systematic application of geostatistical analysis coupled with process optimization, geological risks associated with grade uncertainties are minimized, and an optimized operational sequence for the extraction of mineral blocks is pursued. The main point of this approach is maintaining grade distributions aligned with the historical mean grade, with low variability. The use of genetic algorithms optimizes block selection by incorporating the location, interaction, and efficiency of shovels as seed points. The results demonstrate that the methodology generates weekly schedules that enhance the stationarity of grade distribution, thereby improving operational efficiency and reducing risks