  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.
