神经网络算法在物流配送车辆优化调度中应用

Improved Neural Network Algorithm in Logistics Vehicle Scheduling Application

研究物流配送车辆调度优化问题,车辆调度存在空驶率,运输路径不合理。为了有效节约车辆运输成本,优化城市车辆调度,传统的调度算法存在计算复杂度高,不利于实际应用等问题,提出了一种改进的神经网络车辆调度优化算法模型。首先对城市车辆调度建立优化数学模型,建立了一种解决非满载车辆卸货路线优化的神经网络模型,采用改进的神经网络进行优化车辆调度,并给出了解决配送车辆优化调度问题的具体步骤。仿真结果表明,提出的改进的算法不仅能有效地求解车辆调度优化模型,而且计算机复杂度较低,算法的计算效率较高,收敛速度较快,验证了改进算法的实用性和有效性。

The paper studied Vehicle Routing and Scheduling Optimization. In order to effectively save vehicle transportation costs, optimize the urban vehicle scheduling, an improved neural network model was proposed as the vehicle routing optimization algorithm. First, the paper established urban vehicle scheduling optimization model and a fully loaded vehicle to address non-discharge route optimization neural network model Then, the improved neural network was used to optimize the vehicle scheduling, and the specific steps were given for the delivery vehicle scheduling. Simulation results show that the improved algorithm can effectively solve the vehicle scheduling optimization model, and the computer is of low complexity, high computational efficiency, and fast convergence, which verifies the practicability and effectiveness of the algorithm.