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

arXiv: https://arxiv.org/abs/2503.21115 · HTML: https://mlsanity.com/paper/2503.21115/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 24 references that are in the map.
- First posted on arXiv 27 Mar 2025.

## Builds on (most cited first, 5 of 5)

- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [EconLogicQA: A Question-Answering Benchmark for Evaluating Large Language Models in Economic Sequential Reasoning](https://mlsanity.com/paper/2405.07938/) (2024)
- [InvAgent: A Large Language Model based Multi-Agent System for Inventory Management in Supply Chains](https://mlsanity.com/paper/2407.11384/) (2024)
- [AI in Supply Chain Risk Assessment: A Systematic Literature Review and Bibliometric Analysis](https://mlsanity.com/paper/2401.10895/) (2023)
- [Stochastic Service Network Design with Different Operational Patterns for Hyperconnected Relay Transportation](https://mlsanity.com/paper/2402.06222/) (2024)

## Built on by (most cited first, 3 of 3)

- [CRMAgent: A Multi-Agent LLM System for E-Commerce CRM Message Template Generation](https://mlsanity.com/paper/2507.08325/) (2025)
- [OptiLoop: Coordination-in-the-Loop Verification and Repair for LLM-Generated Optimization Agents](https://mlsanity.com/paper/2605.27630/) (2026)
- [AutoMARS: Automatic and Scalable Multi-Agent Large Language Model Framework for Supply Chain Risk Assessment](https://mlsanity.com/paper/2610.17206/) (2026)

## Abstract (arXiv)

The growing emphasis on energy efficiency and environmental sustainability in global supply chains introduces new challenges in the deployment of hyperconnected logistic hub networks. In current volatile, uncertain, complex, and ambiguous (VUCA) environments, dynamic risk assessment becomes essential to ensure successful hub deployment. However, traditional methods often struggle to effectively capture and analyze unstructured information. In this paper, we design an Large Language Model (LLM)-driven risk assessment pipeline integrated with multiple analytical tools to evaluate logistic hub deployment. This framework enables LLMs to systematically identify potential risks by analyzing unstructured data, such as geopolitical instability, financial trends, historical storm events, traffic conditions, and emerging risks from news sources. These data are processed through a suite of analytical tools, which are automatically called by LLMs to support a structured and data-driven decision-making process for logistic hub selection. In addition, we design prompts that instruct LLMs to leverage these tools for assessing the feasibility of hub selection by evaluating various risk types and levels. Through risk-based similarity analysis, LLMs cluster logistic hubs with comparable risk profiles, enabling a structured approach to risk assessment. In conclusion, the framework incorporates scalability with long-term memory and enhances decision-making through explanation and interpretation, enabling comprehensive risk assessments for logistic hub deployment in hyperconnected supply chain networks.