Our work is the first attempt to apply Natural Language Processing to automate the development of simulation models of systems vitally important for logistics. We demonstrated that the framework built on top of the fine-tuned GPT-3 Codex, a Transformer-based language model, could produce functionally valid simulations of queuing and inventory control systems given the verbal description. In conducted experiments, GPT-3 Codex demonstrated convincing expertise in Python as well as an understanding of the domain-specific vocabulary. As a result, the language model could produce simulations of a single-product inventory-control system and single-server queuing system given the domain-specific context, a detailed description of the process, and a list of variables with the corresponding values. The demonstrated results, along with the rapid improvement of language models, open the door for significant simplification of the workflow behind the simulation model development, which will allow experts to focus on the high-level consideration of the problem and holistic thinking.

翻译：本研究首次尝试运用自然语言处理技术，自动化构建对物流领域至关重要的系统仿真模型。我们证明，基于微调后的GPT-3 Codex（一种基于Transformer的语言模型）构建的框架，能够根据文本描述生成功能有效的排队系统和库存控制系统仿真。实验显示，GPT-3 Codex不仅展现出令人信服的Python编程能力，还表现出对领域特定词汇的理解。在给定领域上下文、流程详细描述及变量对应值的情况下，该语言模型能够成功生成单产品库存控制系统和单服务台排队系统的仿真程序。这些研究成果，结合语言模型的快速迭代，为显著简化仿真模型开发流程开辟了新路径，使专家能够将精力集中于问题的高层次思考与全局性分析。