Simulation Optimization of Shovel-Truck System in Open-Pit Mines Considering Rockmass Parameters

Abstract

The shovel-truck system remains a popular method for overburden removal and mineral excavation in open-pit mines, needing rigorous logistical management to achieve required productivity levels and maximize resource utilization. Fixed truck assignment (FTA) models represent a prevalent method for truck allocation in open-pit mining, owing to their simplified fleet operational management. However, existing FTA models often overlook the simultaneous minimization of both trucks’ waiting time and shovels’ idle time. Consequently, these oversights lead to suboptimal allocation of trucks to shovels, resulting in either trucks queuing or shovels idling while awaiting trucks. Such inefficiencies contribute to fleet underutilization and increased fuel costs. To tackle the above issue, this research introduces a novel truck dispatching rule, MFTA, which integrates geotechnical parameters and excavating equipment performance to optimize truck allocation in open-pit mining. Geotechnical parameters across various rock and soil formations reveal significant variability, influencing shovel performance assessed through the total loading time (TLT) indicator. Utilizing TLT and travel times of loaded and empty trucks, the study determines the optimal number of fixed trucks allocated to each shovel by minimizing the total waiting time (TWT). A case study conducted in an open-pit coal mine in Thar, Pakistan, validates the approach, demonstrating that adjusting truck allocations based on TLT significantly reduces operational inefficiencies and enhances productivity. The findings highlight the effectiveness of this method in improving overall operational efficiency and economics in open-pit mining. Integrating real-time data and advanced simulation techniques, this research enhances the competitiveness and sustainability of mining operations. These outcomes are particularly relevant for mining professionals aiming to optimize mining operations for improved efficiency and sustainability.