Optimization modeling via mixed-integer linear programming (MILP) is fundamental to industrial planning and scheduling, yet translating natural-language requirements into solver-executable models and maintaining them under evolving business rules remains highly expertise-intensive. While large language models (LLMs) offer promising avenues for automation, existing methods often suffer from low data efficiency, limited solver-level validity, and poor scalability to industrial-scale problems. To address these challenges, we present EvoOpt-LLM, a unified LLM-based framework supporting the full lifecycle of industrial optimization modeling, including automated model construction, dynamic business-constraint injection, and end-to-end variable pruning. Built on a 7B-parameter LLM and adapted via parameter-efficient LoRA fine-tuning, EvoOpt-LLM achieves a generation rate of 91% and an executability rate of 65.9% with only 3,000 training samples, with critical performance gains emerging under 1,500 samples. The constraint injection module reliably augments existing MILP models while preserving original objectives, and the variable pruning module enhances computational efficiency, achieving an F1 score of ~0.56 on medium-sized LP models with only 400 samples. EvoOpt-LLM demonstrates a practical, data-efficient approach to industrial optimization modeling, reducing reliance on expert intervention while improving adaptability and solver efficiency.

翻译：基于混合整数线性规划（MILP）的优化建模是工业规划与调度的基础，然而将自然语言需求转化为求解器可执行模型，并在不断演化的业务规则下进行维护，仍高度依赖专家知识。尽管大语言模型（LLM）为自动化提供了可行途径，但现有方法常面临数据效率低、求解器级别有效性有限以及工业规模问题可扩展性差等挑战。为解决这些问题，我们提出EvoOpt-LLM——一个统一的基于LLM的框架，支持工业优化建模的全生命周期，包括自动模型构建、动态业务约束注入以及端到端变量剪枝。该框架基于7B参数LLM，并通过参数高效的LoRA微调进行适配，仅使用3,000个训练样本即可达到91%的生成率和65.9%的可执行率，关键性能增益在1,500个样本内出现。约束注入模块在保持原始目标函数的同时可靠地扩充现有MILP模型，变量剪枝模块则提升了计算效率，仅需400个样本即可在中等规模LP模型上实现约0.56的F1分数。EvoOpt-LLM展示了一种实用且数据高效的工业优化建模方法，在减少专家干预的同时提升了适应性与求解器效率。