A Genetic Algorithm Model for Short-Term Planning and Quality Management in Open-Pit Mining

Operational (short-term) planning in open-pit mining is a critical phase for ensuring grade control and production stability, particularly in complex geological environments. While long-term plans define the strategic goals, they often overlook shift-level variability and operational constraints of a shovel-truck system. This paper presents an optimization model based on a genetic algorithm (GA) for shift-by-shift operational planning. The model integrates real-world technological constraints of the equipment used, including fixed shift capacity (2000 t) and various constraints characteristic of active mining locations. The fitness function is designed to minimize the deviations from the targeted quality range for iron (Fe: 47–50%) and silica (SiO2: ≤11%), while ensuring rational use of mineral reserves. The model was tested on a case study involving eight limonite ore open pits over a period of one production year (1000 shifts). The results show that the GA-generated plan reaches quality requirements in 98.1% of all shifts. This GA approach provides more balanced mining operations and confirms and ensures the achievement of goals from long-term plans, reducing the reliance on large-scale homogenization stockpiles. The developed tool is implemented in Excel/VBA and offers a practical framework for mining engineers to work with.