AIDA: a tool for resiliency in smart manufacturing
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, consideri...
| Main Authors: | , , , , , |
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| Format: | Conference item |
| Language: | English |
| Published: |
Springer Nature
2023
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| _version_ | 1811139672655527936 |
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| author | De Giacomo, G Favorito, M Leotta, F Mecella, M Monti, F Silo, L |
| author_facet | De Giacomo, G Favorito, M Leotta, F Mecella, M Monti, F Silo, L |
| author_sort | De Giacomo, G |
| collection | OXFORD |
| description | 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. |
| first_indexed | 2024-04-23T08:26:42Z |
| format | Conference item |
| id | oxford-uuid:fd3d660a-1ee0-4429-b00d-50ceef176bf8 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-09-25T04:09:49Z |
| publishDate | 2023 |
| publisher | Springer Nature |
| record_format | dspace |
| spelling | oxford-uuid:fd3d660a-1ee0-4429-b00d-50ceef176bf82024-06-10T10:47:09ZAIDA: a tool for resiliency in smart manufacturingConference itemhttp://purl.org/coar/resource_type/c_5794uuid:fd3d660a-1ee0-4429-b00d-50ceef176bf8EnglishSymplectic ElementsSpringer Nature2023De Giacomo, GFavorito, MLeotta, FMecella, MMonti, FSilo, LOne 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. |
| spellingShingle | De Giacomo, G Favorito, M Leotta, F Mecella, M Monti, F Silo, L AIDA: a tool for resiliency in smart manufacturing |
| title | AIDA: a tool for resiliency in smart manufacturing |
| title_full | AIDA: a tool for resiliency in smart manufacturing |
| title_fullStr | AIDA: a tool for resiliency in smart manufacturing |
| title_full_unstemmed | AIDA: a tool for resiliency in smart manufacturing |
| title_short | AIDA: a tool for resiliency in smart manufacturing |
| title_sort | aida a tool for resiliency in smart manufacturing |
| work_keys_str_mv | AT degiacomog aidaatoolforresiliencyinsmartmanufacturing AT favoritom aidaatoolforresiliencyinsmartmanufacturing AT leottaf aidaatoolforresiliencyinsmartmanufacturing AT mecellam aidaatoolforresiliencyinsmartmanufacturing AT montif aidaatoolforresiliencyinsmartmanufacturing AT silol aidaatoolforresiliencyinsmartmanufacturing |