多金属露天矿多目标生产计划优化问题建模及求解算法

针对多金属露天矿山生产计划优化问题难以建模、求解复杂等问题，从多种金属元素、采掘运输成本以及矿石品位三个角度出发，综合考虑矿石产量、品位波动、矿石资源利用率、开采和处理能力以及回采率等多种影响因素，构建了一个多金属露天矿多目标生产计划模型。受粒子群算法启发，提出一种改进狼群算法（IGWO）对模型进行求解，并引入反向学习策略和非线性收敛策略来提高算法的求解效率。以国内某露天矿的实际生产为例，分别利用粒子群算法（PSO）、灰狼算法（GWO）和IGWO算法对模型进行求解对比。结果表明：该生产计划模型更加符合露天矿多种矿产资源综合开采利用的实际需求，IGWO算法较PSO算法运行速度上提高了71%，在求解精度上提高16%。该生产计划方案对多金属露天矿山矿产资源综合利用及精细化排产具有重要的指导意义，可促进企业可持续发展。

Modeling and Algorithm of Multi-objective Production Scheduling Optimization for Multi-metal Open-pit Mine

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.