Medium-Term Planning of Mining Complexes with Explicit Shovel Tracking and Processing Plant Uncertainty

Abstract

Simultaneous and stochastic optimization of open-pit mining complexes at the medium-term level aims to maximize expected profits, manage technical risk for integrated value chains, and enhance the operational feasibility of the long-term plan while still meeting its targets to achieve long-term value. To address two key operational feasibility challenges over a twelve-month horizon, the proposed framework integrates two features that provide a more detailed operational evaluation during decision-making than existing methods. The first involves explicit tracking of shovel movements to align optimized extraction sequences with the operational capabilities of loading equipment. The second uses high-order simulation to produce probability distributions for processing plant responses based on geometallurgical properties of the material being scheduled and selected operating modes. To efficiently optimize schedules with this detailed evaluation, a solution method is proposed using an online machine learning model to rank moves in a metaheuristic search procedure. The framework is demonstrated using a gold mining complex and results show realistic extraction sequences with an increase in metal production and cashflow when compared to a regression-based processing model.