This research introduces an LLM-driven framework for dynamic risk assessment in logistics hub deployment, addressing modern supply chain challenges in volatile environments.

• Integrates LLMs to analyze unstructured data for comprehensive risk evaluation
• Enhances decision-making for sustainable and energy-efficient logistics networks
• Provides adaptive risk assessment capabilities in geopolitically unstable regions
• Delivers practical engineering solutions for optimized hub deployment

For engineering teams, this approach offers a significant advancement over traditional methods by incorporating real-time data analysis and contextual understanding of complex risk factors across interconnected supply chains.

Leveraging Large Language Models for Risk Assessment in Hyperconnected Logistic Hub Network Deployment