Optimization using discrete event simulation and mixed integer programming: application on haulage systems for deep underground mines

Abstract: The application of discrete event simulation for the optimization of the haulage methods of underground operations at great depth is presented. The discrete event simulation was carried out to evaluate four haulage methods for the improvement of the overall mine production and a minimizing of the operating costs. Other techniques can be applied to achieve the same objective but discrete event simulation is known for its advantage of more accurately accounting for real world uncertainty and diversity. Discrete event simulation is then combined with mixed integer programming to improve decision-making in the process of generating and optimizing the mine plans associated with each hauling option. The haulage system is one of the most important operations in underground mines as it involves the transportation of the mined out material from the draw points to the processing plant. When the depth increases, hauling of ore from deeper levels need to be evaluated in order to account for the constraints, configuration and current utilization of the ore handling system for improvement of productivity and operations. The increase in mine depth affects many factors among which are the increases in haulage distance from mine areas to the mine surface. The increase in haul distance results in an increase in the energy cost of the specific hauling equipment. The haulage process is one of the most energy-intensive activities in a mining operation and thus one of the main contributors to energy cost. This research uses the combination of discrete event simulation and mixed integer programing to compare the operating values of the mine plans generated for an orebody at depth levels of 1,000, 2,000, and 3,000 meters for diesel and electric trucks, shaft and belt conveyor haulage systems for the current and future energy prices. The results shows that, in comparison with analytical methods, discrete event simulation combined with Mixed Integer Programming (MIP) is faster and generates a more feasible solution, increases the understanding of the behavior of various systems, and reduces risk when selecting the operational systems. It is indicated that the energy cost increases as the mine depth increases and it differs for each haulage method for both current and future energy prices with higher costs in diesel trucks and lowest costs when using a shaft haulage system. The energy costs for diesel trucks account for 38.2%, 46.8% and 63.1% of operating costs at the current energy price, and 64.9%, 72.5% and 83.7% of operating costs at the future energy prices at the 1,000, 2,000 and 3,000 meter depth levels respectively, while the energy cost for the shaft haulage system accounts for 10.8%, 13.0% and 15.4% of operating costs at the current energy price, and for 26.6%, 30.9% and 35.4% of operating costs at the future energy price at the 1,000, 2,000 and 3,000 meter depth levels respectively. The energy costs is further analyzed based on haulage costs as a percentage of the total operating cost for all options, and the results show that diesel truck haulage is substantially more expensive compared to other haulage options with least energy cost on shaft haulage system with increasing depth. This study thus provides mining companies operating at great depths, a broad and up-to-date analysis of the impact on energy costs on the haulage methods as the mine depth increases.