基于改进遗传算法的露天矿运输路径优化

为克服露天矿以等效运距为权重进行路径优化的现实不可行性,建立以时变运输功最小化为优化目标的路径优化模型,用以描述道路网络的随机波动性。首先建立了时变运输功计算模型,并进一步提出基于随机过程的最优化概率模型;最后从改造遗传算法入手,进一步提高解的收敛速度。实验结果显示,较之传统的静态路径规划算法,文中算法不仅能快速获得低能耗的最优化路径,而且能大幅度的提高解的收敛速度,进一步压缩整个运输系统实际工程中的费用成本。算法在实际调度过程中具有更高效的优化效果。

Open-pit path optimization based on improved genetic algorithm

In order to solve the impractical problem of path optimization in open-pit mines adoping equivalent haulage distance as weight coefficient, we establish an optimization modeling with the aim of minimizing transport work path to describe the stochastic fluctuation in real open-pit road network. Firstly, a calculation model of transport work is established, and then a probability optimization model based on stochastic process is proposed. Finally, the convergence speed of solution is further improved from the aspect of modifying genetic algorithm. Compared with other algorithms, the experimental results show that this algorithm can not only quickly obtain low energy consumption optimization path, but also greatly improve the convergence speed of solution. Thus the cost of the whole transport network can be reduced. The algorithm is more efficient in the process of actual scheduling.