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An integrated data- and capability-driven approach to the reconfiguration of agent-based production systems
Adalat, Omar Afazov, S. Ratchev, S.
Adalat, Omar
Afazov, S.
Ratchev, S.
Publication Date
2023-01
End of Embargo
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Rights
© The Author(s) 2022. Open Access - This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Peer-Reviewed
Yes
Open Access status
openAccess
Accepted for publication
2022-11-15
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Abstract
Industry 4.0 promotes highly automated mechanisms for setting up and operating flexible manufacturing systems, using distributed control and data-driven machine intelligence. This paper presents an approach to reconfiguring distributed production systems based on complex product requirements, combining the capabilities of the available production resources. A method for both checking the “realisability” of a product by matching required operations and capabilities, and adapting resources is introduced. The reconfiguration is handled by a multi-agent system, which reflects the distributed nature of the production system and provides an intelligent interface to the user. This is all integrated with a self-adaptation technique for learning how to improve the performance of the production system as part of a reconfiguration. This technique is based on a machine learning algorithm that generalises from past experience on adjustments. The mechanisms of the proposed approach have been evaluated on a distributed robotic manufacturing system, demonstrating their efficacy. Nevertheless, the approach is general and it can be applied to other scenarios.
Version
Published version
Citation
Scrimieri D, Adalat O, Afazov S et al (2022) An integrated data- and capability-driven approach to the reconfiguration of agent-based production systems. International Journal of Advanced Manufacturing Technology. 124: 115-1168.
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Article
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Notes
Research Development Fund Publication Prize Award winner, Nov 2022