基于蚁群算法冷链物流运输路径最优化设计

Optimization design of cold chain logistics transportationpath based on ant colony algorithm

目的:研究冷链物流车辆调度及运输路径的最优化设计。方法:以冷链运输过程中的车辆为监测对象,建立车辆调度模型,分别使用传统的蚁群算法和改进的蚁群算法研究调度车辆运输成本与运输时间。同时对冷链物流车调度算法进行仿真优化,利用蚁群算法进行路径最优化设计。结果:基于蚁群算法的车辆调度模型以及算法的仿真实验表明,在相同的条件下,改进的蚁群算法能有效改善车辆调度,冷链物流车的行驶距离明显缩短。结论:改进的蚁群算法能调度算法能够找到距离优、成本低的路径进行车辆调度,有效降低冷链物流产品运输成本和保证产品的质量。

Aims:This paper studies the optimal design of cold chain logistics vehicle scheduling and transportation routes.Methods:Taking the vehicles in the cold chain transportation as the monitoring object,a vehicle scheduling model was established.The traditional ant colony algorithm and the improved ant colony algorithm were used to study the transportation cost and transportation time of the vehicles.At the same time,the cold chain logistics vehicle scheduling algorithm was simulated and optimized.The ant colony algorithm was used to optimize the path design.Results:The vehicle scheduling model based on the ant colony algorithm and the simulation experiment of the algorithm showed that under the same conditions the improved ant colony algorithm could effectively improve the vehicle scheduling.And the travel distance of the cold chain logistics vehicle was significantly shortened.Conclusions:The improved ant colony algorithm scheduling algorithm can find the route with the best distance and lower cost for vehicle scheduling,thus effectively reducing the transportation cost of cold chain logistics products and ensuring the quality of products.