Optimal path planning and data simulation of emergency material distribution based on improved neural network algorithm

Abstract

With the development of computer technology 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 has become a reality to build a system for the optimal distribution of emergency materials. The processing of big data can realize the prediction of future emergency materials storage and transportation. In this paper, 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 the structural parameters of the neural network algorithm, Using genetic algorithms to construct predictions, and combined with the actual purpose of material distribution after disasters occur. 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.

Distribution vehicle +drone joint distribution path optimization based on K-means optimization genetic algorithm

“…To find the best delivery route, we should aim to deliver all nodes as quickly as possible under the premise that all points are delivered by vehicles only once [4] .…”

“…Liu Wusheng et al [3] studied the overall path optimization problem of "drone-vehicle" joint distribution, and the influence of the drone's maximum load and flight distance on the drone's distribution capability, but did not consider the load limit of the vehicle's capacity. Han Mengyi et al [4] studied the optimization of emergency material distribution path, and the proposed hybrid genetic algorithm is better than the standard genetic algorithm in both the solution process and the solution results, but only the vehicles are delivered separately.…”