物流配送车辆优化调度仿真研究
发布时间：2018-11-15 12:55
【摘要】：传统物流配送车辆调度研究存在目标单一,约束条件考虑有限,路径规划不合理等问题,不利于实际应用。调度优化可有效节约资源,提升企业运营效益。为了降低配送车辆的距离和时间总成本,提高求解算法的效率和精度,提出一种适用型整数规划模型和改进型最大最小蚁群算法。首先建立了针对时变环境、带时间窗、限制车辆容量等约束条件的车辆优化调度模型,采用结合时变策略的改进型最大最小蚁群算法求解调度模型,并给出了具体实现流程。以Solomon测试集对算法性能进行测试,仿真结果表明,改进型最大最小蚁群算法具有较高的求解精度和收敛速度,适用型模型及算法实用地、有效地优化了物流配送车辆的调度问题。
[Abstract]:The traditional research on vehicle scheduling of logistics distribution has some problems, such as single objective, limited consideration of constraint conditions, unreasonable path planning, and so on, which is not conducive to practical application. Scheduling optimization can effectively save resources and improve the efficiency of enterprise operation. In order to reduce the distance and total time cost of the distribution vehicle and improve the efficiency and accuracy of the algorithm, a suitable integer programming model and an improved maximum and minimum ant colony algorithm are proposed. Firstly, a vehicle optimal scheduling model with time-varying environment, time window and limited vehicle capacity is established. An improved maximum and minimum ant colony algorithm combined with time-varying strategy is used to solve the scheduling model, and the implementation flow is given. The Solomon test set is used to test the performance of the algorithm. The simulation results show that the improved maximum and minimum ant colony algorithm has higher accuracy and convergence speed, and the applicable model and algorithm are practical. The vehicle scheduling problem of logistics distribution is effectively optimized.
【作者单位】： 北京邮电大学自动化学院;
【基金】：国家“十二五”科技支撑计划(014BAD10B06)
【分类号】：TP18;U116.2
本文编号：2333364
[Abstract]:The traditional research on vehicle scheduling of logistics distribution has some problems, such as single objective, limited consideration of constraint conditions, unreasonable path planning, and so on, which is not conducive to practical application. Scheduling optimization can effectively save resources and improve the efficiency of enterprise operation. In order to reduce the distance and total time cost of the distribution vehicle and improve the efficiency and accuracy of the algorithm, a suitable integer programming model and an improved maximum and minimum ant colony algorithm are proposed. Firstly, a vehicle optimal scheduling model with time-varying environment, time window and limited vehicle capacity is established. An improved maximum and minimum ant colony algorithm combined with time-varying strategy is used to solve the scheduling model, and the implementation flow is given. The Solomon test set is used to test the performance of the algorithm. The simulation results show that the improved maximum and minimum ant colony algorithm has higher accuracy and convergence speed, and the applicable model and algorithm are practical. The vehicle scheduling problem of logistics distribution is effectively optimized.
【作者单位】： 北京邮电大学自动化学院;
【基金】：国家“十二五”科技支撑计划(014BAD10B06)
【分类号】：TP18;U116.2
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