大型物流车辆配送线路自适应调度方法

Adaptive Scheduling Method for Large-scale Logistics Distribution Vehicle Lines

为了有效提高物流配送车辆的利用率,降低配送车辆的空载率及物流运输成本,需要对大型物流车辆配送线路自适应调度方法进行研究;当前方法多是采用通过对物流车辆配送过程中的调度与路径选择进行分析,建立多类型的物流配送车辆调度模型,并构建改进后的遗传算法,对物流调度模型的算法效率以及计算时间和复杂度进行优化,以获取物流车辆配送调度问题的最优解,但该方法存在过程较为繁琐的问题;为此,提出一种大型物流车辆配送线路自适应调度方法;该方法首先建立物流车辆配送线路调度问题的数学模型,为实现自适应调度方法对数学模型进行优化求出最优解,利用蚁群算法对物流车辆配送调度数学模型的最优解进行优化,获取最优路径的适应度初始化蚁群算法的各客户点之间的信息素,从而得出了优化的最优路径;以优化的最优路径完成对大型物流车辆配送线路自适应调度;仿真实验表明,利用蚁群算法不仅加快了物流配送路线调度优化问题求解的速度,降低了物流运输的成本,而且获取了最优解的概率,比其他调度算法具有更明显的优势。

In order to effectively improve the utilization rate of logistics distribution vehicle and reduce the empty loading rate of distribu- tion vehicles and logistics transportation cost, need adaptive scheduling method for large--scale logistics distribution vehicle lines were stud led. But the current method is used to in the process of logistics distribution vehicle scheduling and the choice of path analysis, many model in the process of logistics distribution vehicle scheduling model is established, on the basis of constructing the improved genetic algorithm, the efficiency of logistics scheduling model and algorithm to optimize and reduce the computing time and complexity and more efficient to obtain the optimal solution of logistics distribution vehicle scheduling problem, but the process of this method were more complicated problems. For this, put forward a kind of large--scale logistics distribution vehicle line adaptive scheduling method. This method first to establish a logistics distribution line adaptive scheduling, the mathematical model for the realization of the adaptive scheduling method to further optimization of the mathematical model to find the optimal solution, using the mathematical model of ant colony algorithm for logistics distribution vehicle scheduling optimization, the optimal solution to obtain the optimal path of ant colony algorithm to initialize the fitness of each customer infor- mation between the point element, thus obtained the further optimization of the optimal path; In order to optimize the optimal path to com- plete the adaptive scheduling for large logistics vehicle distribution lines. Simulation experiments show that using ant colony algorithm not on- ly speeds up the logistics distribution vehicle route scheduling optimization problem solving speed, reduce the cost of logistics, and obtain the optimal solution of probability, scheduling algorithm has more obvious advantages than others.