Ontology-Based Feedback to Improve Runtime Control for Multi-Agent Manufacturing Systems

• Published in: 2023 IEEE 19th International Conference on Automation Science and Engineering (CASE) pp. 1 - 7
• Main Authors: Lim, Jonghan, Pfeiffer, Leander, Ocker, Felix, Vogel-Heuser, Birgit, Kovalenko, Ilya
• Format: Conference Proceeding
• Language: English
• Published: IEEE 26.08.2023
• Subjects: Automation; Behavioral sciences; Computer aided software engineering; Knowledge based systems; Ontologies; Productivity; Runtime
• ISSN: 2161-8089
• DOI: 10.1109/CASE56687.2023.10260621

Improving the overall equipment effectiveness (OEE) of machines on the shop floor is crucial to ensure the productivity and efficiency of manufacturing systems. To achieve the goal of increased OEE, there is a need to develop flexible runtime control strategies for the system. Decentralized strategies, such as multi-agent systems, have proven effective in improving system flexibility. However, runtime multi-agent control of complex manufacturing systems can be challenging as the agents require extensive communication and computational efforts to coordinate agent activities. One way to improve communication speed and cooperation capabilities between system agents is by providing a common language between these agents to represent knowledge about system behavior. The integration of ontology into multi-agent systems in manufacturing provides agents with the capability to continuously update and refine their knowledge in a global context. This paper contributes to the design of an ontology for multi-agent systems in manufacturing, introducing an extendable knowledge base and a methodology for continuously updating the production data by agents during runtime. To demonstrate the effectiveness of the proposed framework, a case study is conducted in a simulated environment, which shows improvements in OEE during runtime.