Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
97 tokens/sec
GPT-4o
53 tokens/sec
Gemini 2.5 Pro Pro
44 tokens/sec
o3 Pro
5 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Ontology-Based Feedback to Improve Runtime Control for Multi-Agent Manufacturing Systems (2309.10132v1)

Published 18 Sep 2023 in cs.MA

Abstract: 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.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (24)
  1. S. Nakajima, “Introduction to TPM: Total Productive Maintenance.” Productivity Press, p. 129, 1988.
  2. R. Iannone and M. E. Nenni, “Managing OEE to Optimize Factory Performance,” Operations Management, pp. 31–50, 2013.
  3. I. Kovalenko, D. Tilbury, and K. Barton, “The Model-based Product Agent: A Control Oriented Architecture for Intelligent Products in Multi-agent Manufacturing Systems,” Control Engineering Practice, vol. 86, pp. 105–117, 2019.
  4. J.-H. Lee and C.-O. Kim, “Multi-agent Systems Applications in Manufacturing Systems and Supply Chain Management: A Review Paper,” International Journal of Production Research, vol. 46, no. 1, pp. 233–265, 2008.
  5. F. Ocker, I. Kovalenko, K. Barton, D. Tilbury, and B. Vogel-Heuser, “A Framework for Automatic Initialization of Multi-Agent Production Systems Using Semantic Web Technologies,” IEEE Robotics and Automation Letters, vol. 4, no. 4, pp. 4330–4337, 2019.
  6. M. Obitko and V. Marik, “Ontologies for Multi-agent Systems in Manufacturing Domain,” 13th International Workshop on Database and Expert Systems Applications, IEEE Computer Society, pp. 597–602, 2002.
  7. T. R. Gruber, “A Translation Approach to Portable Ontology Specifications,” Knowledge acquisition, vol. 5, no. 2, pp. 199–220, 1993.
  8. S. Lemaignan, A. Siadat, J.-Y. Dantan, and A. Semenenko, “MASON: A Proposal for an Ontology of Manufacturing Domain,” IEEE Workshop on Distributed Intelligent Systems: Collective Intelligence and Its Applications, pp. 195–200, 2006.
  9. M. Dinar and D. W. Rosen, “A Design for Additive Manufacturing Ontology,” Journal of Computing and Information Science in Engineering, vol. 17, no. 2, 2017.
  10. Q. Cao, S. Beden, and A. Beckmann, “A Core Reference Ontology for Steelmaking Process Knowledge Modelling and Information Management,” Computers in Industry, vol. 135, p. 103574, 2022.
  11. E. Järvenpää, N. Siltala, O. Hylli, and M. Lanz, “The Development of an Ontology for Describing the Capabilities of Manufacturing Resources,” Journal of Intelligent Manufacturing, vol. 30, no. 2, pp. 959–978, 2019.
  12. O. Kovalenko, I. Grangel-González, M. Sabou, A. Lüder, S. Biffl, S. Auer, and M.-E. Vidal, “Automationml Ontology: Modeling Cyber-physical Systems for Industry 4.0,” IOS Press Journal, vol. 1, 2018.
  13. P. Leitão, “Agent-based Distributed Manufacturing Control: A State-of-the-art Survey,” Engineering Applications of Artificial Intelligence, vol. 22, no. 7, pp. 979–991, 2009.
  14. P. Leitao, V. Marik, and P. Vrba, “Past, Present, and Future of Industrial Agent Applications,” IEEE Transactions on Industrial Informatics, vol. 9, no. 4, pp. 2360–2372, 2013.
  15. H. J. Yoon and W. Shen, “A Multiagent-Based Decision-Making System for Semiconductor Wafer Fabrication With Hard Temporal Constraints,” IEEE Transactions on Semiconductor Manufacturing, vol. 21, no. 1, pp. 83–91, 2008.
  16. M. Bi, I. Kovalenko, D. M. Tilbury, and K. Barton, “Dynamic Resource Allocation Using Multi-Agent Control for Manufacturing Systems,” IFAC-PapersOnLine, vol. 54, no. 20, pp. 488–494, 2021.
  17. J. Barbosa, P. Leitão, E. Adam, and D. Trentesaux, “Dynamic Self-organization in Holonic Multi-agent Manufacturing Systems: The ADACOR Evolution,” Computers in Industry, vol. 66, pp. 99–111, 2015.
  18. A. M. Farid and L. Ribeiro, “An Axiomatic Design of a Multiagent Reconfigurable Mechatronic System Architecture,” IEEE Transactions on Industrial Informatics, vol. 11, no. 5, pp. 1142–1155, 2015.
  19. M. Bi, I. Kovalenko, D. M. Tilbury, and K. Barton, “Dynamic Distributed Decision-making for Resilient Resource Reallocation in Disrupted Manufacturing Systems,” International Journal of Production Research, pp. 1–21, 2023.
  20. S. Borgo and P. Leitão, “The Role of Foundational Ontologies in Manufacturing Domain Applications,” OTM Confederated International Conferences, Springer, pp. 670–688, 2004.
  21. P. Vrba, M. Radakovič, M. Obitko, and V. Mařík, “Semantic Technologies: Latest Advances in Agent-based Manufacturing Control Systems,” International Journal of Production Research, vol. 49, no. 5, pp. 1483–1496, 2011.
  22. J. Nielsen, “Information Modeling of Manufacturing Processes: Information Requirements for Process Planning in a Concurrent Engineering Environment,” Ph.D. dissertation, KTH, 2003.
  23. J.-B. Lamy, “Owlready: Ontology-oriented Programming in Python with Automatic Classification and High Level Constructs for Biomedical Ontologies,” Artificial intelligence in medicine, vol. 80, pp. 11–28, 2017.
  24. Stardog Union, “Stardog: The Enterprise Knowledge Graph Platform.” [Online]. Available: https://www.stardog.com/
User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (5)
  1. Jonghan Lim (6 papers)
  2. Leander Pfeiffer (1 paper)
  3. Felix Ocker (10 papers)
  4. Birgit Vogel-Heuser (22 papers)
  5. Ilya Kovalenko (11 papers)

Summary

We haven't generated a summary for this paper yet.