Abstract: In view of the difficulties in modeling and the complexity in solving problems during optimization of production plan in multi-metal open-pit mine， a multi-objective production plan of multi-metal open-pit mine is constructed from the perspectives of various metal elements， mining and transportation costs and ore grade， and by taking into account various factors such as ore output， grade fluctuation， utilization rate of ore resources， mining and processing capacity and recovery rate. Inspired by particle swarm optimization （PSO）， an improved wolf swarm algorithm （IGWO） is proposed to solve the model， and the reverse learning strategy and nonlinear convergence strategy are introduced to improve the efficiency of the algorithm. Taking the actual production of a domestic open pit mine as a case， particle swarm optimization （PSO）， gray wolf algorithm （GWO） and IGWO algorithm are respectively used to solve the model and are compared. The results show that the proposed production planning model is more in line with the actual needs of the comprehensive exploitation and utilization of various mineral resources in open-pit mines. The IGWO algorithm is 71% faster than the PSO algorithm， with the accuracy of the solution increasing 16%. The production plan has important guiding significance for the comprehensive utilization of mineral resources and fine production scheduling of multi-metal open-pit mines， and can promote the sustainable development of enterprises.

Key words: Open pit mines, Production scheduling, Multi-metal, Multi-objective, Improved Grey Wolf Optimization（IGWO）