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The ASSISTANT project: AI for high level decisions in manufacturing
Insight Centre for Data Analytics, University College Cork, Cork, Ireland.
IMT Atlantique, LS2N-CNRS, Nantes, France.
Laboratory for Manufacturing Systems Automation, University of Patras, Patras, Greece.
CodesignS, Flanders Make vzw, Lommel, Belgium.
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2023 (English)In: International Journal of Production Research, ISSN 0020-7543, E-ISSN 1366-588X, Vol. 61, no 7, p. 2288-2306Article in journal (Refereed) Published
Abstract [en]

This paper outlines the main idea and approach of the H2020 ASSISTANT (LeArning and robuSt deciSIon SupporT systems for agile mANufacTuring environments) project. ASSISTANT is aimed at the investigation of AI-based tools for adaptive manufacturing environments, and focuses on the development of a set of digital twins for integration with, management of, and decision support for production planning and control. The ASSISTANT tools are based on the approach of extending generative design, an established methodology for product design, to a broader set of manufacturing decision making processes; and to make use of machine learning, optimisation, and simulation techniques to produce executable models capable of ethical reasoning and data-driven decision making for manufacturing systems. Combining human control and accountable AI, the ASSISTANT toolsets span a wide range of manufacturing processes and time scales, including process planning, production planning, scheduling, and real-time control. They are designed to be adaptable and applicable in a both general and specific manufacturing environments.

Place, publisher, year, edition, pages
Taylor & Francis, 2023. Vol. 61, no 7, p. 2288-2306
Keywords [en]
Artificial intelligence, data analytics, digital twins, process and production planning, reconfigurable manufacturing systems, scheduling and real-time control
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:umu:diva-198345DOI: 10.1080/00207543.2022.2069525ISI: 000828974100001Scopus ID: 2-s2.0-85134609832OAI: oai:DiVA.org:umu-198345DiVA, id: diva2:1685072
Available from: 2022-08-01 Created: 2022-08-01 Last updated: 2023-07-14Bibliographically approved

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Östberg, Per-Olov

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CiteExportLink to record
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