车辆相互干扰情况下物流集中配送的交通调度方法

Method of Traffic Dispatching of Centralized Logistics Distribution under Vehicle Interference

传统物流车辆调度模型在复杂的路况以及车辆相互干扰情况下,存在调度时间长、无法提供实时调度的弊端,导致模型中不同级别调度信号失调,调度算法存在冲突、效率较低的缺陷。针对物流集中配送,提出了基于遗传算法以及差异数据融合的物流车辆调度方法,采用混合整数规划方法塑造物流车辆调度的模型,在优化指标中融入惩罚项目,塑造其他车辆干扰情况下物流车辆调度的目标函数,采用遗传算法针对车辆调度的目标函数获取最佳车辆调度结果,通过差异数据融合车辆调度补偿模型,对其它车辆干扰情况下的物流车辆调度的误调、漏调等问题进行准确的修正,对物流车辆进行合理的调度。仿真结果显示,该种方法可对复杂的物流配送路况以及车辆堵塞情况进行合理的调度,车辆调度的性能优于传统方法,具有较强的应用价值。

In this paper, in view of the deficiency of the traditional logistics vehicle dispatching model, we proposed the logistics vehicle dispatching method based on GA and differential data fusion, formulated the logistics vehicle dispatching model using the hybrid integer programming, constructed the objective function of logistics vehicle dispatching under the interference of other vehicles, and then applied the genetic algorithm to the objective function to obtain the optimal vehicle dispatching result. Next through a simulation experiment, we found that the method could cope with complex road condition and traffic congestion situations and was more effective than the traditional method.