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Assistant: Learning and robust decision support system for agile manufacturing environments

Beldiceanu, Nicolas (author)
IMT Atlantique, LS2N-CNRS, Nantes, France
Dolgui, Alexandre (author)
IMT Atlantique, LS2N-CNRS, Nantes, France
Gonnermann, Clemens (author)
TUM, Munich, Germany
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Gonzalez-Castañé, Gabriel (author)
University College Cork, Ireland
Kousi, Niki (author)
Laboratory for Manufacturing Systems and Automation, University of Patras, Patras, Greece
Meyers, Bart (author)
CodesignS, Flanders Make vzw, Belgium
Prud'homme, Julien (author)
IMT Atlantique, LS2N-CNRS, Nantes, France
Thevenin, Simon (author)
IMT Atlantique, LS2N-CNRS, Nantes, France
Vyhmeister, Eduardo (author)
University College Cork, Ireland
Östberg, Per-Olov (author)
Umeå universitet,Institutionen för datavetenskap
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 (creator_code:org_t)
Elsevier, 2021
2021
English.
In: IFAC-PapersOnLine. - : Elsevier. ; , s. 641-646
  • Conference paper (peer-reviewed)
Abstract Subject headings
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  • The European project ASSISTANT will provide a set of AI-based digital twins that helps process engineers and production planners to operate collaborative mixed-model assembly lines based on the data collected from IoT devices and external data sources. Such a tool will help planners to design the assembly line, plan the production, operate the line, and improve process tuning. In addition, the system monitors the line in real-time, ensures that all required resources are available, and allows fast re-planning when necessary. ASSISTANT aims to make cost-effective decisions while ensuring product quality, safety and wellbeing of the workers, and managing the various sources of uncertainties. The resulting digital twin systems will be data-driven, agile, autonomous, collaborative and explainable, safe but reactive.

Subject headings

NATURVETENSKAP  -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
NATURAL SCIENCES  -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
TEKNIK OCH TEKNOLOGIER  -- Maskinteknik -- Produktionsteknik, arbetsvetenskap och ergonomi (hsv//swe)
ENGINEERING AND TECHNOLOGY  -- Mechanical Engineering -- Production Engineering, Human Work Science and Ergonomics (hsv//eng)

Keyword

Artificial intelligence
Data analytics
Decision aid
Digital twins
Process and production planning
Real-time control
Reconfigurable manufacturing systems
Scheduling

Publication and Content Type

ref (subject category)
kon (subject category)

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