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

Nicolas Beldiceanu; Alexandre Dolgui; Clemens Gonnermann; Gabriel Gonzalez-Castañé; Niki Kousi; Bart Meyers; Julien Prud'homme; Simon Thevenin; Eduardo Vyhmeister; Per Olov Östberg

doi:10.1016/j.ifacol.2021.08.074

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

Nicolas Beldiceanu
, Alexandre Dolgui
, Clemens Gonnermann
, Gabriel Gonzalez-Castañé
, Niki Kousi
, Bart Meyers
, Julien Prud'homme
, Simon Thevenin
, Eduardo Vyhmeister
, Per Olov Östberg

UBL
Technical University of Munich
University of Patras
Flanders Make
Umeå University

Research output: Contribution to journal › Article › peer-review

Abstract

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.

Original language	English
Pages (from-to)	641-646
Number of pages	6
Journal	IFAC-PapersOnLine
Volume	54
Issue number	1
DOIs	https://doi.org/10.1016/j.ifacol.2021.08.074
Publication status	Published - 2021
Event	17th IFAC Symposium on Information Control Problems in Manufacturing INCOM 2021 - Budapest, Hungary Duration: 7 Jun 2021 → 9 Jun 2021

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

Keywords

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

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10.1016/j.ifacol.2021.08.074

Cite this

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title = "Assistant: Learning and robust decision support system for agile manufacturing environments",

abstract = "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.",

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

author = "Nicolas Beldiceanu and Alexandre Dolgui and Clemens Gonnermann and Gabriel Gonzalez-Casta{\~n}{\'e} and Niki Kousi and Bart Meyers and Julien Prud'homme and Simon Thevenin and Eduardo Vyhmeister and {\"O}stberg, \{Per Olov\}",

note = "Publisher Copyright: {\textcopyright} 2021 The Authors. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0); 17th IFAC Symposium on Information Control Problems in Manufacturing INCOM 2021 ; Conference date: 07-06-2021 Through 09-06-2021",

year = "2021",

doi = "10.1016/j.ifacol.2021.08.074",

language = "English",

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T2 - 17th IFAC Symposium on Information Control Problems in Manufacturing INCOM 2021

AU - Beldiceanu, Nicolas

AU - Dolgui, Alexandre

AU - Gonnermann, Clemens

AU - Gonzalez-Castañé, Gabriel

AU - Kousi, Niki

AU - Meyers, Bart

AU - Prud'homme, Julien

AU - Thevenin, Simon

AU - Vyhmeister, Eduardo

AU - Östberg, Per Olov

PY - 2021

Y1 - 2021

N2 - 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.

AB - 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.

KW - Artificial intelligence

KW - Data analytics

KW - Decision aid

KW - Digital twins

KW - Process and production planning

KW - Real-time control

KW - Reconfigurable manufacturing systems

KW - Scheduling

UR - https://www.scopus.com/pages/publications/85119693666

U2 - 10.1016/j.ifacol.2021.08.074

DO - 10.1016/j.ifacol.2021.08.074

M3 - Article

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SN - 2405-8971

VL - 54

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EP - 646

JO - IFAC-PapersOnLine

JF - IFAC-PapersOnLine

IS - 1

Y2 - 7 June 2021 through 9 June 2021

ER -

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

Abstract

UN SDGs

Keywords

Access to Document

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