Citation link: http://dx.doi.org/10.25819/ubsi/9966
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Dokument Type: Article
metadata.dc.title: Bibliometric study on the use of machine learning as resolution technique for Facility Layout Problems
Authors: Burggräf, Peter 
Wagner, Johannes 
Heinbach, Benjamin 
Institute: Fakultät IV - Naturwissenschaftlich-Technische Fakultät 
Free keywords: Bibliometrische Studie, Anlagenlayoutprobleme, Bibliometric Study, Facility Layout Problems
Dewey Decimal Classification: 620 Ingenieurwissenschaften und zugeordnete Tätigkeiten
GHBS-Clases: ZHX
Issue Date: 2021
Publish Date: 2021
Source: IEEE Access ; vol. 9, S. 22569-22586. - DOI: https://doi.org/10.1109/ACCESS.2021.3054563
Abstract: 
Facility Layout Problems (FLP) are concerned with finding efficient factory layouts. Numerous resolution approaches are known in literature for layout optimization. Among those, intelligent approaches are less researched than solutions from exact or approximating approaches. The recent surge of research interest in Artificial Intelligence, and specifically Machine Learning (ML) techniques, presages an increase of such techniques' usage in FLP. However, previous reviews on FLP research induce that, to date, this trend has not yet emerged. Utilizing a systematic literature review coupled with a k-Means based clustering algorithm, we analyzed 25 relevant publication full-texts from an original sample of 1,425 papers. Our findings corroborate the statement that ML techniques have attracted substantially less research interest than most other resolution approaches. While a few papers used Unsupervised Learning algorithms directly as a solution to the FLP, Supervised and Reinforcement Learning were found to be practically irrelevant. ML usage was significantly higher in FLP-adjacent planning tasks such as group technology. Drawing from experiences with other NP-hard combinatorial optimization problems in manufacturing research, we conclude that Reinforcement Learning is most promising to bridge the evident gap between FLP and ML research. Our study further contributes to FLP research by extending established classification frameworks.
Description: 
Finanziert aus dem Open-Access-Publikationsfonds der Universität Siegen für Zeitschriftenartikel
DOI: http://dx.doi.org/10.25819/ubsi/9966
URN: urn:nbn:de:hbz:467-19528
URI: https://dspace.ub.uni-siegen.de/handle/ubsi/1952
Appears in Collections:Publikationen aus der Universität Siegen

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