Our research strives to examine how simulation models of logistics systems can be produced automatically from verbal descriptions in natural language and how human experts and artificial intelligence (AI)-based systems can collaborate in the domain of simulation modelling.We demonstrate that a framework constructed upon the refined GPT-3 Codex is capable of generating functionally valid simulations for queuing and inventory management systems when provided with a verbal explanation.As a result, the language model could produce simulation models for inventory and process control.These results, along with the rapid improvement of language models, enable a significant simplification of simulation model development.Our study offers guidelines and a design of a natural language processing-based framework on how to build simulation models of logistics systems automatically, given the verbal description.In generalised terms, our work offers a technological underpinning of human-AI collaboration for the development of simulation models.
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Jackson et al. (2023) studied this question.
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