TY  - JOUR
AB  - 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.
AU  - Burggräf, Peter
AU  - Wagner, Johannes
AU  - Heinbach, Benjamin
DA  - 2021
DO  - 10.25819/ubsi/9966
KW  - Flexible Fertigung
KW  - Bibliometrische Studie
KW  - Anlagenlayoutprobleme
KW  - Bibliometric Study
KW  - Facility Layout Problems
LA  - eng
PY  - 2021
TI  - Bibliometric study on the use of machine learning as resolution technique for Facility Layout Problems
UR  - https://nbn-resolving.org/urn:nbn:de:hbz:467-19528
Y2  - 2024-11-22T05:11:11
ER  -