An Intelligent Logistics Scheduling System Based on Big Data and IoT for Cross-Border Trade: A Case Study of China-Armenia Trade Corridor

The rapid expansion of cross-border trade under the Belt and Road Initiative has created unprecedented demands for efficient and intelligent logistics management. Traditional logistics systems, however, struggle to meet the requirements of real-time tracking, dynamic route optimization, and cost-effective operations across international borders. This research proposes an intelligent logistics scheduling system that integrates Internet of Things (IoT) sensors, edge computing, and big data analytics to address these challenges. The proposed system represents a critical stepping stone toward fully autonomous freight transportation networks, where intelligent scheduling algorithms coordinate fleets of connected and eventually self-driving vehicles. Focusing on the China-Armenia trade corridor as a representative case study, our system employs a multi-layered architecture comprising a perception layer with GPS, RFID, and environmental sensors; an edge computing layer for real-time data preprocessing; and a cloud-based intelligent scheduling module utilizing genetic algorithms for dynamic route optimization. The system simultaneously optimizes multiple objectives including transportation cost, delivery time, and fuel consumption. Through simulation experiments based on actual trade route data, our proposed system demonstrates a reduction of approximately 18% in total logistics costs and a 22% improvement in on-time delivery rates compared to conventional scheduling methods. Furthermore, economic analysis reveals that the system achieves a return on investment of 2.3 within the first operational year.