Natural language-driven production planning: integrating large language models with automatic simulation model generation in manufacturing systems

作者：Mohaiad Elbasheer, Y. Laili, F. Longo, Vittorio Solina, Yiran Tao, Pierpaolo Veltri, Yuteng Zhang, Lin Zhang · 发表于：Journal of Intelligent Manufacturing · 年份：2025 · DOI：10.1007/s10845-025-02732-z · 被引用次数：16

Simulation modeling provides powerful decision support capabilities for production planning in manufacturing systems, yet widespread adoption remains limited by significant barriers in model development and maintenance. Traditional simulation approaches require substantial expertise, time investment, and financial resources, creating accessibility challenges particularly for small and medium enterprises. Current research on automatic simulation generation overlooks the significant potential that generative AI can offer in bridging the gap between natural language expression and executable model creation. This paper presents a novel methodology integrating large language models (LLMs) with automatic simulation model generation (ASMG) to enable direct generation of executable simulation models from natural language conversations. Unlike existing ASMG approaches that require formal specifications or simulation knowledge, our framework reduces programming requirements by allowing users to create functional models through conversational interaction. Our approach leverages natural language processing capabilities to transform user requests into executable simulation models without requiring specialized programming knowledge, enhancing human–machine interaction in production planning environments. The framework implements a four-step process: user intent expression through natural language interfaces, knowledge extraction using structured templates, simulation model construction via...