摘要
为了提高露天矿卡车运输效率,有效降低矿山企业开采成本,针对量子粒子群算法(QPSO)在优化求解过程中易陷入局部最优的问题,本研究引入惯性权自适应调整的量子粒子群优化算法(DCW-QPSO),并借助于遗传算法的变异操作,将DCW-QPSO的粒子更新方法改进,然后将改进DCW-QPSO用于求解露天矿卡车运输调度方案。调度方案以总运输成本最低为目标函数,并综合考虑矿石产量、品位均衡、卡车等待时间最短等约束条件。通过在国内某大型露天铁矿的应用发现,卡车调度优化结果符合矿山实际生产需求,为企业管理者提高了决策依据。改进算法在模型求解过程中全局搜索能力及算法稳定性都得到显著提升,具有一定的实际应用价值。
In order to improve the efficiency of truck transport in open-pit mines and effectively reduce the mining cost of enterprises,aiming at the problem that quantum particle swarm optimization(QPSO) is easy to fall into local optimization in the optimization process,the quantum particle swarm optimization algorithm(dcw-qpso) with self-adaptive adjustment of inertia weight was introduced to improve the particle update method of dcw-qpso by the mutation operation of genetic algorithm.After then,the improved dcw-qpso is adopted to solve the truck transportation dispatching scheme of open pit mine. The dispatching scheme takes the lowest total transportation cost as the objective function,and comprehensively considers the ore output,the grade balance and the shortest waiting time of truck as constraints. Through applying it in a large-scale open-pit iron mine in China,it is found that the optimization results of truck scheduling meet the actual production needs of the mine.It provides the decision-making basis for enterprise managers and significantly improves the global search ability and algorithm stability in the process of model solution for the improved algorithm,with a certain practical application value.
作者
王俊栋
李宁
吴亚辉
卢文杰
王李管
李江江
Wang Jundong;Li Ning;Wu Yahui;Lu Wenjie;Wang Liguan;Li Jiangjiang(School of Resource and Environment Engineering,Wuhan University of Technology,Wuhan 430070,China;Hubei Key Laboratory of Mineral Resources Processing and Environment,Wuhan 430070,China;School of Resource and Safety Engineering,Central South University,Changsha 410083,China)
出处
《金属矿山》
CAS
北大核心
2019年第12期156-162,共7页
Metal Mine
基金
中央高校基本科研业务费专项资金项目（编号：2019III086CG,2019-ZH-B1-14,2018IVB054）
