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From natural language to simulations: applying AI to automate simulation modelling of logistics systems

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  • Ilya Jackson
  • Maria Jesus Saenz
  • Dmitry Ivanov

Abstract

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.

Suggested Citation

  • Ilya Jackson & Maria Jesus Saenz & Dmitry Ivanov, 2024. "From natural language to simulations: applying AI to automate simulation modelling of logistics systems," International Journal of Production Research, Taylor & Francis Journals, vol. 62(4), pages 1434-1457, February.
    • Handle: RePEc:taf:tprsxx:v:62:y:2024:i:4:p:1434-1457
    DOI: 10.1080/00207543.2023.2276811
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    Cited by:

    1. Ivanov, Dmitry, 2024. "Cash flow dynamics in the supply chain during and after disruptions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 185(C).

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