Digital Library

Speech-to-jobshop: an ontology-driven digital assistant for simulation modeling

• Heiner Ludwig
• Vincent Betker
• Thorsten Schmidt
• Mathias Kuehn

(2024). ECMS 2024, 38th Proceedings
Edited by: Daniel Grzonka, Natalia Rylko, Grazyna Suchacka, Vladimir Mityushev, European Council for Modelling and Simulation.
DOI: http://doi.org/10.7148/2024
ISSN: 2522-2422 (ONLINE)
ISSN: 2522-2414 (PRINT)
ISSN: 2522-2430 (CD-ROM)
ISBN: 978-3-937436-84-5
ISBN: 978-3-937436-83-8 (CD) Communications of the ECMS Volume 38, Issue 1, June 2024, Cracow, Poland June 4th – June 7th, 2024

https://doi.org/10.7148/2024-0143

Heiner ludwig, Vincent betker, Thorsten schmidt, Mathias kuehn (2024). Speech-To-Jobshop: An Ontology-Driven Digital Assistant For Simulation Modeling, ECMS 2024, Proceedings Edited by: Daniel Grzonka, Natalia Rylko, Grazyna Suchacka, Vladimir Mityushev, European Council for Modelling and Simulation. doi:10.7148/2024-0143

This paper introduces a novel method utilizing speech-based digital assistants and large language models (LLMs) to streamline the creation of simulation models for Job Shop Scheduling Problems (JSSP). The system simplifies the process by allowing natural language interactions for ontology-based model generation. The study evaluates the performance of various LLMs in ontology-based simulation modeling by benchmarking their ability to extract and assign semantical entities and relations. We found that ChatGPT-4-Turbo is able to correctly identify all model elements given in descriptions of the production scenarios we tested, while less resource-intensive and open source models like Mixtral-8x7b and Zephyr-beta perform well in a less complex scenario. The findings demonstrate the potential of integrating LLMs and natural language processing in simulation modeling, significantly enhancing efficiency and reducing the need for manual modeling.