Chen Min
School of Computer Science and Engineering, Hunan University of Information Technology, Changsha, 410151 Hunan China.
Soft comput. 2023;27(9):5995-6005. doi: 10.1007/s00500-023-08073-4. Epub 2023 Apr 3.
Today, data storage technology is also gradually improving. Various industries can store massive amounts of data for analysis. The global climate change and the bad ecology led to frequent occurrence of natural disasters. Therefore, it is necessary to establish an effective emergency materials distribution system. The neural network model is used to calculate and the optimal emergency distribution route is analyzed according to the historical information and the data. Considering backpropagation, this paper further disposing a method to further improve the calculation of neural network algorithm. From the perspective of structural parameters of neural network algorithms, this paper uses genetic algorithms to construct predictions, and combines the actual purpose of material distribution after disasters. Considering the capacity constraints of distribution centers, time constraints, material needs of disaster relief points and different means of transportation, a dual-objective path planning with multiple distribution centers and multiple disaster relief points with the shortest overall delivery time and lowest overall delivery cost is constructed. By establishing an emergency material distribution system, it can maximize the prompt and accurate delivery after a natural disaster occurs, and solves the urgent needs of the people.
如今，数据存储技术也在逐步改进。各个行业都能够存储海量数据用于分析。全球气候变化和恶劣的生态环境导致自然灾害频发。因此，有必要建立一个有效的应急物资配送系统。利用神经网络模型进行计算，并根据历史信息和数据来分析最优应急配送路线。考虑到反向传播，本文进一步提出一种方法来进一步改进神经网络算法的计算。从神经网络算法的结构参数角度出发，本文使用遗传算法来构建预测，并结合灾后物资配送的实际目的。考虑配送中心的容量限制、时间限制、救灾点的物资需求以及不同的运输方式，构建了一个具有多个配送中心和多个救灾点的双目标路径规划，以实现总交付时间最短和总交付成本最低。通过建立应急物资配送系统，能够在自然灾害发生后最大限度地实现快速、准确的交付，解决民众的迫切需求。
