城市交通车辆调度优化建模研究仿真

发布时间：2018-07-29 21:18

【摘要】：在车辆调度建模时,由于突发性和不规律性,无法形成稳定的可预测状态。传统的车辆调度网络中,缺少必要的预报机制,导致调度存在较大的滞后性,调度时间过长。提出改进经纬格任务的遗传算法,利用经纬格性能体系结构的预报机制,改良初始种群生成方式,提高遗传算法衍生率减少迭代次数,减少运算提高运行速度。对车辆调度任务提出了适应度函数,满足改进算法对调度任务的适应度。实验结果表明,提出的改进优化经纬格任务遗传算法提高了调度的性能,更优于传统调度策略。
[Abstract]:In vehicle scheduling modeling, it is impossible to form a stable and predictable state due to sudden and irregular characteristics. In the traditional vehicle scheduling network, the lack of necessary forecasting mechanism leads to a large lag and long scheduling time. An improved genetic algorithm for warp and weft lattice tasks is proposed. By using the prediction mechanism of warp and weft lattice performance architecture, the initial population generation method is improved, the derivation rate of genetic algorithm is increased to reduce the number of iterations and the speed of operation is reduced. The fitness function of vehicle scheduling task is proposed to satisfy the fitness of the improved algorithm. The experimental results show that the proposed genetic algorithm improves the scheduling performance and is better than the traditional scheduling strategy.
【作者单位】：中北大学机电工程学院;
【分类号】：U492.22;TP18

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