Year: 2026
Type: conference-paper
Abstract: To address two correlated question in Optimization under Uncertainty (OuU): Expertise Threshold and Selection Conundrum, we propose LLM4OuU, a multi-agent framework that automates both the modeling and solving of six distinct types of uncertainty models and generates mapping pairs to explore the potential relationship between optimization problems and optimal models.Firstly, we decompose the complex modeling process into five sequential steps and design specialized LLM agents combining high-level domain expertise.Secondly, we introduce a hybrid dataset spanning various industries based on Retrieval-Augmented Generation (RAG) to benchmark performance.Extensive experiments demonstrate that LLM4OuU achieves superior performance compared to baselines, even reaching up to 99% on specific model types.Finally, we establish a mapping from problem features to optimal models, with correlation analysis revealing that not only data scale but also the specific scenario significantly influence model selection.Data and code are available: https
Institutions: Nanjing University, Ant Group (China)
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: hybrid
Funder: Ant Group
OpenAlex ID: W7166829909