车祸意外干扰下物流运输网络抗毁模型仿真

Simulation of Survivability Model of Logistics Transportation Network under Interference of Car Accidents

利用传统算法建立物流运输网络,意外情况将造成具备运输能力的路径变为失效路径,从而导致物流运输网络瘫痪。为了避免上述缺陷,提出了一种基于加强型神经网络算法的物流运输网络抗毁方法。利用遗传优化方法,对物流样本数据进行训练,为物流运输网络抗毁设计提供基础。根据加强型神经网络,完成物流运输网络的抗毁设计。实验结果表明,利用该算法进行物流运输网络抗毁设计,能够提高物流运输网络的抗干扰性能。

In this paper, using the traditional algorithm, we established the logistics transportation network in which the occurrence of accidents could turn paths of transportation capacities invalid and thus paralyze the whole logistics transportation network. In order to remedy the above deficiency, we proposed a method to boost the survivability of the logistics network based on reinforced neural network algorithm, used the genetic algorithm to train the sample data. At the end, through an experiment, we found that the method could improve the anti-interference capacity of the logistics transportation network.