In the context of Industry 4.0, the integration of semantic technologies with simulation tools offers a promising approach to enhance production planning and operational decision-making. This paper presents an ontology-coupled discrete-event simulation framework tailored to tire manufacturing. The ontological layer, developed in Web Ontology Language (OWL) and managed via Owlready2 in Python, semantically describes processes, resources, and material flows. This structured knowledge serves as the foundation for a discrete-event simulation (DES) implemented in SimPy, enabling flexible scenario definition and performance evaluation. The ontology layer encodes both the manufacturing schema and the instantiated layout as knowledge graphs. These provide the overarching elements, relations, and data structures that communicate with the DES layer through a wrapper, ensuring a clean separation of concerns. The DES layer models the behavior of active resources through agent-based programming and coordinates the overall execution via an engine that records events and results back into the ontology. Once the simulation is completed, the ontology is further exploited for reasoning and querying to support analysis. The framework is applied to a case study representative of the curing and measurement segment in tire manufacturing and benchmarked against an equivalent model implemented in the commercial software Visual Components. Results show a strong agreement with VC while offering a more transparent and extensible approach to capturing and analyzing manufacturing knowledge thanks to the ontological layer.
Giunta et al. (Thu,) studied this question.