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。
Traditional vehicle scheduling problem is single - goaled with limited constraint conditions, which is not beneficial to practical application. Intelligent dispatching of vehicles can conserve resources effectively and promote benefit. In order to lower total cost of distance and time and increase e^ciency and accuracy of algorithm, we pro- posed a suitable integer programming model and an improved max - min ant colony system algorithm. The research is aiming at constraint conditions such as time -dependent environment,time windows and capacity limitation. Firstly, this paper abstracted critical factors and put forward a targeted mathematical model. Secondly, we presented an im- proved max -min ant colony system algorithm with temporal change strategy. Finally, we adopted Solomon set to test the algorithm. Simulations reveal that the improved method has better performance in solving accuracy and conver- gence rate. The model and algorithm optimize logistic vehicle scheduling problem practically and effectively.
出处
《计算机仿真》
北大核心
2017年第8期179-183,232,共6页
Computer Simulation
基金
国家"十二五"科技支撑计划(014BAD10B06)
关键词
智能交通系统
车辆调度
时变路网
时间窗
最大最小蚁群算法
Intelligent transportation system
Vehicle scheduling
Time varying road network
Time window
Max -min ant colony system algorithm
