Abstract
One of the salient features of Industry 4.0 is that machines and other actors involved in the manufacturing process provide Industrial APIs that allow to inquire their status. In order to provide resilience, the manufacturing process should be able to automatically adapt to new conditions, considering new actors for the fulfillment of the manufacturing goals. As a single manufacturing process may include several of these actors, and their interfaces are often complex, this task cannot be easily accomplished in a completely manual way. In this work, we focus on the orchestration of Industrial APIs using Markov Decision Processes (MDPs). We present a tool implementing stochastic composition of processes and we demonstrate it in an Industry 4.0 scenario.
M. Favorito—The author’s views are his own, and they do not reflect those of his employer.
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Notes
- 1.
Aida is also the name of a famous opera by Verdi, somehow inspired by the making of Suez Canal: undoubtedly an example of smart manufacturing for the time being.
- 2.
See sources at https://github.com/luusi/AIDA.
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Acknowledgements
This work is partially funded by the ERC project WhiteMech (no. 834228), the PRIN project RIPER (no. 20203FFYLK), the Electrospindle 4.0 project (funded by MISE, Italy, no. F/160038/01-04/X41). This study was carried out within the PE1 - FAIR (Future Artificial Intelligence Research) and PE11 - MICS (Made in Italy - Circular and Sustainable) - European Union Next-Generation-EU (Piano Nazionale di Ripresa e Resilienza - PNRR). The work of Flavia Monti is supported by the MISE agreement on “Promozione del progetto della Scuola europea di industrial engineering and management e il sostegno di progetti innovativi di formazione in industrial engineering e management di impresa”.
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De Giacomo, G., Favorito, M., Leotta, F., Mecella, M., Monti, F., Silo, L. (2023). AIDA: A Tool for Resiliency in Smart Manufacturing. In: Cabanillas, C., Pérez, F. (eds) Intelligent Information Systems. CAiSE 2023. Lecture Notes in Business Information Processing, vol 477. Springer, Cham. https://doi.org/10.1007/978-3-031-34674-3_14
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