物流配送车辆最优网络路径选取仿真

Logistics Distribution Vehicle Optimal Network Path Selection Simulation

对物流配送车辆路径的选取,能够有效提高物流配送效率。对物流配送车辆的最优网络路径的选取,需要构建车辆调度数学模型,获取车辆配送特点,完成配送路径的最优化。传统方法利用蚁群算法获取物流配送车辆路径,但忽略了实际交通的动态变化的限制,无法分析车辆配送特点,导致最优路径选取精度偏低。提出基于改进蚁群算法的网络路径获取方法,依据给定的约束条件建立以物流配送成本最低为最优目标的车辆调度数学模型;同时兼顾物流配送车辆的配送特点,与最值蚂蚁系统相结合,对蚁群算法进行了两方面优化,使得蚂蚁的收敛速度和蚂蚁的全局搜索能力得到了提高。将改进后的蚁群算法应用到物流配送车辆最优网络路径获取实际案例中,仿真结果表明,所提方法性能更优,不仅实现了物流配送运输成本最低,还提高了物流配送车辆的利用率和配送效率。

The selection of logistics distribution vehicle route can effectively improve the efficiency of logistics dis- tribution. The traditional method ignores the constraints of dynamic changes in actual traffic, which cannot analyze the characteristics of vehicle distribution, resulting in the low accuracy of optimal path selection. This article focuses on the method of obtaining network route based on improved ant colony algorithm. According to the given constraint, a mathematical model of vehicle scheduling is established which took the lowest cost of logistics distribution as the optimal target. At the same time, by considering the characteristics of logistics distribution vehicles, an ant colony algorithm is optimized from two aspects combined with the ant system. The convergence rate and global search ability of ant are improved. Thus, the improved ant colony algorithm is applied to the actual case that obtains the optimal network path of logistics vehicle. Simulation results show that the proposed method has better performance, which not only achieves the lowest cost of logistics transportation, but also improves the rate of utilization and efficiency of logis- tics distribution.