School of Physics and Electronic Information，Anhui Normal University
Natural Science Foundation of Anhui Province (2008085MF199)
本文针对路网中大规模的货运车辆, 给出一种新的综合油耗成本和时间成本的车辆协同调度方案. 该方案能够在最大化节约运输成本的前提下解决车队的协调优化问题. 首先, 建立基于油耗成本和时间成本的车辆运输成本模型, 通过微调部分车辆的速度与路径, 循环计算任意两辆车组队行驶的运输成本节约率, 将符合组队条件的车辆构造为车辆组队协调图. 然后采用一种基于中心聚类划分的算法将车辆的组队问题转化为聚类求解问题, 求解出节约运输成本最大化的领头车, 使其在公共路段组成车队行驶. 最后, 考虑道路坡度及速度限制等条件,采用基于空间采样的动态规划方法对合并车辆的速度进行优化, 从而进一步降低车辆的运输成本. 仿真结果验证了本文提出的优化方案及求解思路的可行性和有效性.
This paper presents a new cooperative scheduling scheme for large-scale freight vehicles in the road network, which combines fuel consumption cost and time cost. This scheme can solve the problem of vehicle platoon coordination optimization while maximizing transportation cost saving. Firstly, a vehicle transportation cost model based on fuel consumption cost and time cost is established, the transportation cost saving rate of any two vehicles traveling in a group is calculated cyclically by fine-tuning the speed and path of some vehicles, and the vehicles meeting the conditions of the group are constructed as the vehicle group coordination graph. Then, an algorithm based on central clustering division is used to transform the problem of vehicle formation into a cluster solution problem, and the lead vehicle with the maximum transportation cost saving is solved, so that it can form a platoon to drive in public sections. Finally, considering the road slope and speed limit, a dynamic programming method based on spatial sampling is adopted to optimize the speed of the merged vehicles, so as to further reduce the transportation cost of vehicles. Simulation results verify the feasibility and effectiveness of the proposed optimization scheme and solution idea.