整车智能调度运输研究

Research on Intelligent Dispatching and Transportation of Whole Vehicle

为能够考虑整车运输订单的紧急程度选择运输时间或运输费用为最少的运输路径,文中依据整车运输的特点,在平衡运输时间与运输费用的情况下根据汽车企业的情况建立运输模型,模型分成两段:第一段模型,对于整车长距离的跨省运输,依据整车运输订单紧急程度,建立以运输时间或运输费用为权值的邻接矩阵,利用Dijkstra算法计算出从生产基地到各省份的配送中心所需时间成本或运输费用为最少的运输路径;第二段模型,对于整车短距离的省内运输,考虑顾客对收到货物的时间期望而建立以运输费用最少为目标的带时间窗的整车调度运输模型,通过将惯性权重w进行线性递减变换和使用非对称学习因子对基本粒子群算法进行改进,用改进后的粒子群算法对模型进行求解验证。经实证检验,改进后的算法计算出的结果有效降低了运输成本并且减少了运输时间。

In order to consider the urgency of the whole vehicle transportation order,selecting the transportation route with the least transportation time or cost. According to the automobile enterprise’s situation,this paper establishes a transportation model under the condition of balancing transportation time and transportation cost. The model is divided into two sections: The first section of the model is about the long-distance inter-provincial transportation of the whole vehicle. According to the urgency of the whole vehicle transportation order,the adjacency matrix is established,and the transportation time or transportation cost is taken as the weight. This paper uses Dijkstra algorithm to calculate the transportation path with the least time cost or transportation cost from the production base to the distribution center of each province. The second stage of the model is about the short-distance within-provincial transportation of whole vehicle. Considering the customer’s expectation of the time of receiving the goods,thus a whole vehicle transportation model is established with the time window,which aims at the least transportation cost. The basic particle swarm optimization algorithm is improved by linearly decreasing the inertia weight w and using asymmetric learning factors. The improved PSO algorithm is used to solve the model. After empirical test,the results calculated by the improved algorithm effectively reduce the transportation cost and the transportation time.