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An integrated data- and capability-driven approach to the reconfiguration of agent-based production systems

Scrimieri, Daniele; Adalat, Omar; Afazov, Shukri; Ratchev, Svetan


Daniele Scrimieri

Omar Adalat

Shukri Afazov

Cripps Professor of Production Engineering & Head of Research Division


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.


Scrimieri, D., Adalat, O., Afazov, S., & Ratchev, S. (2022). An integrated data- and capability-driven approach to the reconfiguration of agent-based production systems. International Journal of Advanced Manufacturing Technology, 124(3-4), 1155-1168.

Journal Article Type Article
Acceptance Date Nov 15, 2022
Online Publication Date Nov 29, 2022
Publication Date Dec 29, 2022
Deposit Date Feb 2, 2023
Publicly Available Date Feb 2, 2023
Journal The International Journal of Advanced Manufacturing Technology
Print ISSN 0268-3768
Electronic ISSN 1433-3015
Publisher Springer Verlag
Peer Reviewed Peer Reviewed
Volume 124
Issue 3-4
Pages 1155-1168
Keywords Original Article, Reconfiguration, Capabilities, Multi-agent systems, Machine learning, Assembly
Public URL
Publisher URL
Additional Information Received: 21 September 2022; Accepted: 15 November 2022; First Online: 29 November 2022; The authors declare no competing interests.


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