A novel optimization model for the dig-limit definition problem in open pit mines with multiple destinations

Abstract

Dig-limit optimization is a crucial step in the grade-control process for open-pit mines. During short-term planning, blasthole data is used to generate an updated block model, typically, with a finer spatial resolution than the loading equipment's selectivity. Consequently, short-term planners must define operational dig-limits, determining the destination of each Selective Mining Unit (SMU) while accounting for the shovel bucket's size. This task is labor-intensive, highly time-consuming, and prone to SMU misclassification, potentially reducing profit and target fulfillment in the operation. To address these challenges, we propose a novel optimization model for defining optimal dig-limits for open pit mining. The model extends the state-of-the-art by including a wide range of destinations with capacity and blending constraints, and differential selectivity by equipment. Three case studies demonstrate the application of the model in short- and medium-term planning setups. Results indicate an average profit improvement of 7.3% compared to manually drawn dig-limits. Additionally, capacity and blending constraints significantly influence the optimal SMU assignment in scenarios with multiple destinations. The proposed model is computationally more efficient than existing exact methods, solving larger benches and complex setups to optimality using an off-the-shelf solver. This enhanced efficiency and versatility make the model a valuable tool for improving grade-control workflows in open-pit mining operations.