Abstract: According to the problems of large unit costs of mining and transportation as well as deviated results at metal mining enterprises, firstly, depending on the principle of production scheduling for metal mining enterprises, the minimizing cost of ore mining and transportation is seen as optimization goal, and with an integer programming method, a mathematical model of metal mining enterprise production planning is constructed based on the goal of minimizing the cost of mining and transportation. Secondly, an improve quantum particle swarm optimization is proposed, to accurately and quickly solve the production scheduling model of metal mining enterprises, the inertial weights are dynamically adjusted by using the evolution speed and aggregation factor, and a search strategy of dual-layer feasible region is designed for improve local and global search capabilities of algorithm. Finally, we take a large-scale mining and transportation operations of metal mining enterprises as a case, and compare with the actual production indicators, non-linear programming results and results of particle swarm optimization. The results show that the algorithm in this paper is superior to the other two optimization methods under the same economic index and parameters, and the mining and transportation unit costs per ton ore is reduced by about 0.05 yuan, which improves the overall economic profits of metal mining enterprises.

Key words: metal mine, mineenterprises, production scheduling, improved quantum particle swarm optimization

摘要： 针对金属矿山企业的单位开采与运输成本大、优化求解结果偏差大问题, 首先, 依据金属矿山企业编制开采计划的基本原则, 以矿石开采与运输成本最小化为优化目标, 利用整数规划方法, 构建了金属矿山企业生产计划数学模型, 其次, 为了精准快速求解金属矿山企业生产计划模型, 提出了改进的量子粒子群优化算法, 采用进化速度和聚集度因子对算法中的惯性权重进行动态调整, 并设计了双层可行域搜索策略, 提高了算法的局部和全局搜索能力。最后, 以某大型金属矿山企业采运生产作业为案例, 通过与矿山实际生产指标、非线性规划结果以及粒子群优化结果进行比较分析。结果表明:在相同经济指标和参数环境下, 本文算法优于其它两种优化方法, 且每吨矿石的开采和运输成本减少了0.05元左右, 降低了金属矿山企业的开采运输成本, 提高了企业的整体经济效益。

关键词: 金属矿山, 矿山企业, 生产计划, 改进的量子粒子群算法