Abstract:Purpose: In order to reduce the waste of mineral resources and improve the utilization rate of lean ore, considering the influence of insufficient ore surplus and the limited amount of shovel loading equipment on the selection of loading points in large metal open-pit mines, a loading point selection model was established with the objectives of minimum grade deviation, minimum production cost and shortest total queuing time of vehicles. Method: A two-dimensional matrix integer coding method is adopted, and an indirect quadratic genetic optimization algorithm based on random fragment crossing and specific mutation operators is designed to solve the model. Result: Taking the actual production data of a large metal open-pit mine in China as an example, the simulation results show that the loading point selection scheme can still meet the requirements of the ore allocation plan under the condition that the number of shovel loading equipment is limited and the remaining ore of priority mining is relatively small. Conclusion: The proposed model and algorithm can be generally applied to large-scale metal open-pit mining, and the ore at the loading point can be fully utilized. Significance: It improves the economic benefit of the enterprise and the overall mining efficiency of the mine.