摘要
基于矿山矿道错综复杂,落后的调度方式会使矿车经常进行重复路线运行等问题,本研究以粒子群算法为路径规划核心,通过改变算法惯性权重因子使算法更快获得最优解收敛,加速了算法求解矿车调度模型函数的收敛时间。应用案例表明,该算法结合原有的矿车调度系统,可以在较短时间内自行输出矿车到达运矿点的最优调度策略和行程平均用时。通过此系统规划矿车运行路径,运矿卡车平均每日运输总里程明显减少,降低了无效路程能耗和矿山运矿成本,提高了运输效率。
The complexity of mine tunnels and the backward scheduling mode leads to the repeated running of tramcar.Taking particle swarm optimization(PSO)as the core of path planning,this paper makes the algorithm converge to the optimal solution faster by changing the inertia weight factor of the algorithm,which accelerates the convergence time of the algorithm to solve the model function of tramcar scheduling.The application case shows that the algorithm,combined with the original mine car scheduling system,can automatically output the optimal scheduling strategy and the average travel time of the mine car to the mine transportation point in a short time.Through this system,the running path of mine car is planned,and the average total daily transportation mileage of mine trucks is obviously reduced,which reduces the energy consumption of invalid distance and the cost of mine transportation and improves the transportation efficiency.
作者
袁明才
YUAN Mingcai(Jiangxi Dajishan Tungsten Industry Co.,Ltd.,Ganzhou 341801,Jiangxi,China)
出处
《中国钨业》
CAS
2022年第6期68-74,共7页
China Tungsten Industry
关键词
矿车调度系统
粒子群算法
路径优化
数字化矿山
mine truck dispatching system
particle swarm opitimization
route planning
digitized mine
