From natural language to simulations: applying AI to automate simulation modelling of logistics systems
Name
From natural language to simulations applying AI to automate simulation modelling of logistics systems.pdf
Description
Published version
Size
4.11 MB
Format
Adobe PDF
Checksum (MD5)
99e29e9cc01170b1022222b5d4a1f4f7
Author(s)
Jackson, Ilya
Jesus Saenz, Maria
Ivanov, Dmitry
Date Issued
February 16, 2024
Journal
International Journal of Production Research
Publisher
Taylor & Francis
Citation
Jackson, I., Jesus Saenz, M., & Ivanov, D. (2024). From natural language to simulations: applying AI to automate simulation modelling of logistics systems. International Journal of Production Research, 62(4), 1434–1457.
Version
Final published version
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.
MIT Department
Massachusetts Institute of Technology. Center for Transportation & Logistics
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivatives
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1080/00207543.2023.2276811
