DAOpt: Modeling and Evaluation of Data-Driven Optimization under Uncertainty with LLMs

• URL: http://arxiv.org/abs/2511.11576v1
• Date: Wed, 24 Sep 2025 08:19:28 GMT
• Title: DAOpt: Modeling and Evaluation of Data-Driven Optimization under Uncertainty with LLMs
• Authors: WenZhuo Zhu, Zheng Cui, Wenhan Lu, Sheng Liu, Yue Zhao,
• Abstract summary: Recent advances in large language models (LLMs) have accelerated research on automated optimization modeling.<n>We propose the DAOpt framework including a new dataset OptU, a multi-agent decision-making module, and a simulation environment for evaluating LLMs.
• Score: 16.64448837405414
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advances in large language models (LLMs) have accelerated research on automated optimization modeling. While real-world decision-making is inherently uncertain, most existing work has focused on deterministic optimization with known parameters, leaving the application of LLMs in uncertain settings largely unexplored. To that end, we propose the DAOpt framework including a new dataset OptU, a multi-agent decision-making module, and a simulation environment for evaluating LLMs with a focus on out-of-sample feasibility and robustness. Additionally, we enhance LLMs' modeling capabilities by incorporating few-shot learning with domain knowledge from stochastic and robust optimization.

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