A two-stage simultaneous optimization of NPV and throughput in production planning of open pit mines

Abstract

Open-pit mines are complex businesses with long-term profits ranging from millions to billions of dollars. These mines have a minimum of one discrete (mining) and one continuous (processing) subsystem working in unison to deliver input raw material to several downstream industries. The inherent difference between these two subsystems causes operational challenges in the production process leading to nonoptimal net present value (NPV) and variable quality and quantity of throughput from the discrete to the continuous subsystem. This paper presents a two-stage clustering-MILP algorithm for long-term production planning in open-pit mines. The research integrates multi-range stockpiles in the decision-making process that leads to determining the optimum number of stockpiles required to maximize the discounted value of the mine, as well as balancing the quality and quantity of throughput. We evaluated our developed model in a real open pit mine case study. Results show that with a four-bin stockpile, we can maximize the discounted value of the mine by minimizing head-grade deviation to 5.1% and maximizing the reclaimed material by up to 10.7% of the total ore delivered to the plant.