Location Optimization of Logistics Distribution Center Based on Neural Network

Introduction: The logistics distribution center is a hub that occupies the position of “central nerve” in the whole logistics operation process. Choosing a reasonable logistics distribution center can improve the operation efficiency of the whole logistics system and reduce the cost of workforce and material resources. Objective: The goal of this work is to design a patented technology to optimize the location of the logistics distribution center. Methods: In this patent study, the appropriate clustering algorithm and mathematical model to plan the logistics distribution path are selected to solve the location problem of the distribution center. The location data is input into the neural network model, and the score of the location scheme is output. The mean-shift clustering algorithm is used to divide the geographical location of the merchants, who need to distribute products, and an aggregated distribution area is obtained. Then, a practical mathematical programming model is established to solve the location problem of logistics transfer stations. Results: Comparing the clustering situation of three data point aggregation algorithms, the most reasonable data point aggregation method is obtained. Finally, the data of the location scheme is input into the neural network, and the most perfect logistics distribution location center is selected according to the output score. Conclusion: The establishment of an intelligent logistics distribution center should not only be based on the development needs of the logistics market but also on the development of modern Internet of Things technology.