大数据条件下车辆路径动态优化仿真

Dynamic optimization simulation of vehicle routing under the condition of big data

车辆配送路径的高性能动态优化能够有效降低运输成本、提高顾客满意度,提出了一种基于免疫优化多态蚁群算法的动态车辆路径优化方法。该方法综合考虑运输成本和惩罚费用,构建了带时间窗的动态调度模型。采用多态蚁群算法进行模型求解,引入自适应竞争策略提高全局寻优能力,利用人工免疫算法改进路径搜索过程,提高了寻优精度与速度。仿真实验结果表明,该方法能够有效实现大数据条件下的车辆动态路径优化,较好地解决了寻优精度与速度问题。

High performance dynamic optimization of vehicle routing can effectively reduce transportation costs and improve customer satisfaction.A dynamic vehicle routing optimization method based on immune optimization polymorphic ant colony algorithm is proposed in this paper.Considering transportation cost and penalty cost,a dynamic scheduling model with time window is constructed.Multi state ant colony algorithm is used to solve the model.The adaptive competition strategy is introduced to improve the global optimization ability.The artificial immune algorithm is used to improve the path search process,and the optimization accuracy and speed are improved.The simulation results show that the method can effectively realize the vehicle dynamic path optimization under the condition of big data.And the problem of optimization accuracy and speed is solved.