Optimization of Logistics Management System Based on Genetic Algorithm

Yiliang Lai, Lubin Peng, Longquan Luo, Haidong Hu, Xiaozhu Xie

Abstract: The logistics industry is currently in a developmental phase and faces challenges such as low transportation efficiency and significant losses during the transportation process, which can impact the overall development of the industry. The supply chain, as a core component of the logistics industry, encompasses activities such as sourcing, manufacturing, and distribution. Under specific conditions, all resources within the supply chain can be effectively shared. This condition is facilitated by the explosive development of information technology, which promotes continuous optimization and development of the logistics industry while reducing the loss of information data. Therefore, the application of big data in the logistics industry can significantly improve the stability and security of the entire physical information in practice. In this context, this paper establishes an optimization model for logistics management systems based on the Genetic Algorithm (GA), providing support for the optimization of IoT-driven logistics management systems based on big data. Additionally, the paper describes the construction strategy for combining the Internet of Things and big data in intelligent logistics.

External IDs:dblp:conf/isssr/LaiPLHX24