Business School of Chongqing City Vocational College, Yongchuan, Chongqing 402160, China.
Comput Intell Neurosci. 2022 May 31;2022:1713183. doi: 10.1155/2022/1713183. eCollection 2022.
Logistics distribution vehicle scheduling plays an important role in the supply chain. With the wide application of e-commerce technology and the increasing diversification of urban industrial and commercial development mode, the optimal scheduling of logistics distribution vehicles can improve the economic benefits of logistics and realize the scientization of logistics. Aiming at the problems existing in the logistics allocation system, this paper proposes a logistics allocation system model based on a heterogeneous neural network, uses the heterogeneous neural network to optimize vehicle scheduling, and gives the specific steps to solve the problem of optimal scheduling of distribution vehicles. The simulation consequences exhibit that the proposed technique cannot solely efficaciously clear up the automobile scheduling optimization model and however additionally has low pc complexity, excessive computational effectivity, and speedy convergence speed. The practicability and effectiveness of the improved algorithm are verified. When the number of distribution customers and distribution cycle is the same, the proposed algorithm effectively reduces the total distribution mileage, reduces the number of vehicles, and improves the efficiency of logistics distribution.
物流配送车辆调度在供应链中起着重要作用。随着电子商务技术的广泛应用和城市工商业发展模式的日益多样化，物流配送车辆的优化调度可以提高物流的经济效益，实现物流的科学化。针对物流配送系统中存在的问题，提出了一种基于异构神经网络的物流配送系统模型，利用异构神经网络对车辆调度进行优化，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，所提出的技术不仅能够有效地解决汽车调度优化模型，而且还具有较低的计算机复杂度、较高的计算效率和较快的收敛速度。验证了改进算法的实用性和有效性。当配送客户数量和配送周期相同时，所提出的算法有效地减少了总配送里程，减少了车辆数量，提高了物流配送效率。
