A Stochastic Energy-Efficient Robust Simulation-Based Truck Dispatching Optimization for Simultaneous GHG Mitigation and Operational Excellence in Open-Pit Mines

Abstract

Almost all currently active large open-pit mines across the globe heavily rely on fleet of shovels and haul trucks for material handling. This material handling system has a significant contribution not only to the production cost of the mine (+50%), but also to its energy consumption (+41%) and greenhouse gas (GHG) emissions (+37%). However, traditionally, operation management in general and truck dispatching, in particular, focused primarily on production targets and operational efficiency, neglecting environmental considerations in open-pit mines. Nowadays, growing awareness of climate change and responsible resource extraction has shifted, however, the focus towards sustainable practices throughout the mining industry, open-pit mining not excluded. The significant contribution of this study lies in the development of an integrated simulation and optimization framework that simultaneously accounts for anthropogenic GHG emissions resulting from energy (fuel) consumption and enhances the operational efficiency of the material handling system, thus yielding direct economic and environmental benefits. The paper fulfills its goals with the aim of simultaneous achievement of four interdependent objectives: minimizing the deviations from target production rates set by strategic plans, minimizing the shovel idle time, minimizing the truck wait time, and minimizing the truck fuel consumption. The performance and the robustness of the proposed framework has been evaluated in a case study at Gol-E-Gohar mine in Iran, demonstrating a successful achievement of up to 6% reduction in fuel consumption per tonne of production leading to a significant overall decrease of up to 20,000 liters in fuel consumption, equivalent to 61,000 carbon dioxide (CO2) emissions, in a 10-day operation with 12 hours of operation per day.