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:我们的工作是首次尝试应用自然语言处理系统模拟模型的自动化发展,这些模拟模型对于物流至关重要。我们证明,在经过微调的GPT-3码(以变换器为基础的语言模型)之上建立的框架可以产生功能上有效的排队和库存控制系统模拟(根据口头描述),在进行实验时,GPT-3码(GPT-3码)展示了Python的令人信服的专门知识以及对具体领域的词汇的理解。因此,语言模型可以产生单一产品库存控制系统和单一服务器排队系统的模拟(根据具体领域的背景)、对过程的详细描述以及附有相应价值的变量清单。所显示的结果,连同语言模型的迅速改进,为大量简化模拟模型开发的工作流程打开了大门,这将使专家们能够集中对问题和整体思维进行高级别审议。