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

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.