用PSO-SA算法求解带调拨货的冷链物流车辆配送问题

Using PSO-SA Algorithm to Solve the Distribution Problem of Cold Chain Logistics with Transfer Goods

文中研究了带调拨货的冷链物流车辆配送问题,构建了包括固定成本、运输成本、制冷成本和货损成本的总成本模型,并用启发式算法中的粒子群(PSO)算法和模拟退火(SA)算法协同进行求解,通过模拟实际商业模式构建数据,对重要参数进行经验取值,进行10次实验。实验结果表明:对于带调拨货的冷链物流车辆配送问题,该协同PSO-SA算法可以有效提高车辆的负载使用率,节约配送里程,在可以接受的迭代次数限制内可以收敛到满意解。同时,这种模型也对冷链连锁门店实际配送有一定指导意义。

This paper studies the vehicle distribution problem of cold chain logistics with transfer goods,constructs a total cost model including fixed cost,transportation cost,refrigeration cost and goods damage cost,and uses particle swarm optimization(PSO)algorithm and simulated annealing(SA)algorithm to solve the problem cooperatively.By simulating the actual business model to build data,the important parameters are selected empirically,Ten experiments were carried out.The experimental results show that the collaborative PSO-SA algorithm can effectively improve the vehicle load rate,save the distribution mileage,and converge to a satisfactory solution within the acceptable iterations.At the same time,this model also has some guiding significance for the actual distribution of cold chain chain stores.