基于免疫计算的多车物流配送自适应规划仿真

Adaptive Planning and Simulation of Multi-Vehicle Logistics Distribution Based on Immune Computing

物流配送中的车辆调度是配送管理及决策的关键问题。多车辆的配送过程不仅要考虑车辆的配送效率,还要满足物流的配送时间约束以及车辆的装载率。为实现多车物流配送的自适应规划,引入免疫计算方法,将配送目标看作免疫算子,模仿其在免疫系统中的衍生规律及配送时间、需求点需求等信息。通过权重变换法初步划分待规划任务种群,平衡种群权重值。将物流配送需满足的时间、装载率、行驶距离等作为目标,采用定义法给出对应目标函数。根据免疫计算的遗传寻优步骤,按照目标函数逐一代入,并得出最优的自适应规划方案。仿真结果证明,所提规划方法的车辆配送路径和时间均更短,可以解决多车物流在复杂情况下的配送问题。

At present,the key to distribution management and decision-making is the vehicle scheduling problem in logistics distribution.In order to realize the adaptive planning of multi-vehicle logistics distribution,immune computation was introduced.At first,we regarded the distribution target as an immune operator,and then simulated its derivation law in the immune system,distribution time and demand points.Moreover,we used the weight transformation method to divide the task population to be planned preliminarily and thus to balance the weight value of the population.After taking the time,loading ratio and driving distance required by logistics distribution as the objectives,we gave the objective functions correspondingly by using a definition method.According to the genetic optimization steps of immune computation,we substituted them one by one.Finally,we obtained the optimal adaptive planning scheme.Simulation results show that the proposed method has shorter delivery path and time,so it is able to solve the distribution problem of multi-vehicle logistics in complex situations.