  Optimization problems seek to find the best solution to an objective under a
set of constraints, and have been widely investigated in real-world
applications. Modeling and solving optimization problems in a specific domain
typically require a combination of domain knowledge, mathematical skills, and
programming ability, making it difficult for general users and even domain
professionals. In this paper, we propose a novel framework called OptLLM that
augments LLMs with external solvers. Specifically, OptLLM accepts user queries
in natural language, convert them into mathematical formulations and
programming codes, and calls the solvers to calculate the results for
decision-making. In addition, OptLLM supports multi-round dialogues to
gradually refine the modeling and solving of optimization problems. To
illustrate the effectiveness of OptLLM, we provide tutorials on three typical
optimization applications and conduct experiments on both prompt-based GPT
models and a fine-tuned Qwen model using a large-scale selfdeveloped
optimization dataset. Experimental results show that OptLLM works with various
LLMs, and the fine-tuned model achieves an accuracy boost compared to the
promptbased models. Some features of OptLLM framework have been available for
trial since June 2023 (https://opt.alibabacloud.com/chat or
https://opt.aliyun.com/chat).
