Supply Chain Resilience Perspective: Risk Early Warning and Collaborative Scheduling Mechanism of Agricultural Product Cold Chain Logistics

Abstract

Based on supply chain resilience theory, this paper constructs an integrated mechanism of risk early warning and collaborative scheduling for agricultural product cold chain logistics, providing a systematic framework for improving the reliability of intelligent logistics systems operating in complex electromagnetic and information-intensive environments. Using data such as cold storage utilization rate and transportation loss rate from core nodes including Guangzhou Eastern Railway-Highway Intermodal Hub and Nansha Port, the study completes core risk identification, early warning index system construction, and collaborative scheduling algorithm design by adopting the analytic hierarchy process–entropy weight method, BP neural network, and an improved genetic algorithm. The results show that the proposed three-level early warning index system contains 12 core indicators, while the BP neural network achieves an early warning accuracy of 91.7%. The improved genetic algorithm-based collaborative scheduling model controls the transportation loss rate within 5% under high-risk scenarios and improves the resilience level by 35% compared with traditional scheduling strategies. The constructed resilience-oriented integrated framework provides theoretical support and decision-making guidance for enhancing regional cold chain risk governance and operational efficiency, while offering practical insights for intelligent infrastructure monitoring and reliable logistics operation in digitally connected electromagnetic environments.