Dynamic Monitoring and Emergency Collaborative Decision-Making for Logistics Supply Chain Risks Integrating GD-AHP and MADRL

Abstract

Since there are various uncertain influences that affect the operation of the logistics supply chain, the existing supervision method cannot offer timely and efficient historical data, while the supply-demand relationship still needs enhancement. Therefore, this study puts forward a logistics supply chain risk dynamic monitoring and emergency collaborative decision-making framework based on Group Decision-Analytic Hierarchy Process and Multi-Agent Deep Reinforcement Learning. The framework aims to solve problems such as weak coordination in dynamic evolution and emergency decision-making of modern logistics supply chains, as well as insufficient coupling of multi-source risks. The framework first conducts risk level quantification and real-time monitoring. It combines Temporal Convolutional Network to extract spatiotemporal features and dynamically evaluate supply chain risks. It also introduces Exponentially Weighted Moving Average and other methods to generate emergency collaborative decision-making outputs. According to experimental results, the proposed model can monitor the disruption events accurately with 94.02% and the recall rate with 95.64%. In addition, the early warning delay is only 37.23 ms for monitoring the supply chain disruptions dataset under risk dynamic monitoring. In emergency coordination, the collaborative decision-making reward reaches 1.38 ± 0.10, the disruption loss reduction rate reaches 28.61% ± 8.11%, the resource utilization rate reaches 78.56%, and the task completion rate reaches 91.49%. The results clearly demonstrate that the model is superior to other models in terms of risk recognition accuracy, emergency response delay, resource use efficiency, and decision-making rewards for collaboration. The model is highly efficient in dynamic monitoring and provides effective emergency decision-making support for logistics chains. By improving risk perception, response coordination, and resource allocation efficiency, the proposed framework enhances operational resilience and disruption response capability of logistics chains.