响应用户随机需求下远距离物流配送调度仿真

Simulation of Long-Distance Logistics Distribution Scheduling in Response to Random Demand of Users

由于远距离物流通常涉及多个节点、多个运输模式和不同的运输路径,且需要考虑不同节点之间的时间窗要求,导致调度过程中的信息传递和协调难度增加。为此,提出一种响应用户随机需求的远距离物流配送调度算法。针对用户随机需求信息和经验数据,采用汇集预测方法形成合理的虚拟用户出现概率、坐标位置和需求量信息,以远距离物流配送调度时间最短和用户满意度最高为目标,按照车辆先真实后虚拟用户配送的原则,组建响应用户随机需求的远距离物流配送调度模型。引入改进粒子群(Particle Swarm Optimization,PSO)算法对模型求解,确定最优远距离物流配送调度方案。仿真分析证明,所提算法可以快速响应用户需求,用户满意度均在96%以上,远距离物流配送调度时间最长仅为30min。

Generally,long-distance logistics involves multiple nodes,multiple transportation modes and different transportation paths,so we have to consider the time window requirements between different nodes.The difficulty of information transmission and coordination during the scheduling process will increase as a result.Therefore,a longdistance logistics distribution scheduling algorithm was proposed to respond to the random needs of users.Based on the user's random demand information and experience data,the reasonable occurrence probability of virtual users,coordinate position and demand information were obtained by aggregation and prediction.In order to achieve the shortest scheduling time and the highest user satisfaction in long-distance logistics distribution,a long-distance logistics distribution scheduling model responding to users'random demand was built according to the principle that vehicles first deliver to real users before delivering to virtual users.Finally,an improved Particle Swarm Optimization(PSO)algorithm was introduced to solve the model.Thus,the optimal scheduling scheme was determined.Simulation analysis proves that the proposed algorithm can respond to user demand quickly,and user satisfaction is more than 96%all the time.The longest dispatching time of long-distance logistics is only 30min.