CargoFlow: A Comprehensive System for Congestion Detection and Root Cause Analysis in Cargo Handling

Congestion remains one of the most critical yet inadequately addressed challenges in air cargo material handling systems. The industry currently faces two key limitations: (1) traditional definitions of congestion fail to meet analytical requirements, and (2) root cause analysis relies heavily on manual expert judgment. To address these issues, this paper introduces CargoFlow, a system co-developed with industry partners that integrates advanced search algorithms, large language models (LLMs), and a 3D interactive data visualization interface. The system enables efficient and accurate detection of complex congestion patterns, including those imperceptible to human operators. CargoFlow employs optimized search algorithms to detect intricate congestion patterns efficiently, overcoming the limitations of human-driven inspection. By leveraging advanced LLMs with sophisticated prompt engineering, the system min-imizes human effort in diagnosing root causes. The proposed solution has been deployed and rigorously tested in real-world industrial environments, demonstrating its practical effectiveness.