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Optimisation with multi-objective rule extraction for manufacturing management
University of Skövde, School of Engineering Science. University of Skövde, Virtual Engineering Research Environment. (Production and Automation Engineering)
University of Skövde, School of Engineering Science. University of Skövde, Virtual Engineering Research Environment. (Production and Automation Engineering)
2022 (English)In: International Journal of Manufacturing Research, ISSN 1750-0591, Vol. 17, no 4, p. 452-475Article in journal (Refereed) Published
Abstract [en]

Industry is foreseeing rapid developments in the ability tocapture data within its manufacturing operations and the interest in methodsfor extracting knowledge from such data is increasing. Through digitalrepresentations of manufacturing operations, future scenarios can be modeledand developed with analysis tools based on simulation in combination withmulti-objective optimisation. The results from such analysis tools may bechallenging to interpret, especially when expanding the scope to searchingfor information patterns. An emerging multi-objective rule extraction method,with the ability to handle discrete input parameters, has been furtherdeveloped towards integration in an intelligent decision support system.

Place, publisher, year, edition, pages
InderScience Publishers, 2022. Vol. 17, no 4, p. 452-475
Keywords [en]
simulation, optimisation, decision-support, data mining, rule extraction, manufacturing management
National Category
Other Computer and Information Science
Research subject
Production and Automation Engineering
Identifiers
URN: urn:nbn:se:his:diva-22120DOI: 10.1504/ijmr.2022.127107ISI: 000889321900005Scopus ID: 2-s2.0-85144414345OAI: oai:DiVA.org:his-22120DiVA, id: diva2:1717286
Available from: 2022-12-08 Created: 2022-12-08 Last updated: 2023-01-17Bibliographically approved

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Pehrsson, LeifKarlsson, Ingemar

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  • de-DE
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  • nn-NB
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  • Other locale
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Output format
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  • asciidoc
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