露天矿山运输调度系统粒子群优化

结合露天矿山运输调度系统实际，考虑到计划产量、生产能力、质量要求和运输过程等方面的约束，建立了一个通用性较强的露天矿山运输调度系统模型。该调度模型以总的运营成本最低为目标函数，采用惩罚函数法把诸多约束条件化为无约束，构建优化系统中粒子群算法的适应度函数。通过引入自适应变异程序对粒子群算法进行改进，避免算法过早收敛，提高了收敛效果。运用改进后的自适应变异粒子群算法对露天矿山运输调度系统模型优化求解，并采用Matlab软件对求解过程进行仿真，研究结果表明该算法能够快速得到当前班次的车辆调度的满意方案，提高了露天矿山运输调度的效率，进而提高露天矿山生产率，为数字化矿山建设提供一定参考。

Particle swarm optimization for transportation dispatching system of open-pit mines

Combining with open-pit mine transportation dispatching system,and considering the constraints of scheduled production,productive capacity,quality requirements and transport process,a universal open-pit mine transportation dispatching system model was established.Regarding the minimum total operating costs as the target function,and utilizing the penalty function method to transform large constraints into the unconstrained,the fitness function of particle swarm algorithm was built.Particle swarm algorithm was improved by joining the adaptive mutation process to avoid premature convergence and improve the algorithm convergence effect.Using the improved self-adaptive mutation particle swarm algorithm to solve the optimization problem of open-pit mine transportation dispatching system,adopting the Matlab software to simulate solving process,the results show that the algorithm can get the satisfied program of the current vehicle scheduling,improve the efficiency of open-pit mine transportation scheduling,help to improve the productivity of open-pit mines and provide certain reference for the digital mine construction.