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International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 5/2014

01.10.2014 | Original Article

A model integrating fuzzy AHP with QFD for assessing technical factors in aviation safety

verfasst von: Chuen-Jyh Chen, Shih-Ming Yang, Shih-Chuan Chang

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 5/2014

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Abstract

We developed a model that integrates fuzzy logic, the analytic hierarchy process (AHP), and quality function deployment (QFD) to evaluate the importance weighting of the technical factors in aviation safety, as defined by the International Air Transport Association. AHP evaluates the technical factors using pairwise comparisons, and QFD categorizes the relationship between the criteria and subcriteria using expert knowledge. Based on a questionnaire given to aviation professionals, the model shows that the importance weightings of technical factors “Extensive engine failure, uncontained engine fire,” “Design, manufacture,” and “Engine overheat, propeller failure” are most significant. The result also shows that the over-valued and/or under-valued factors predicted by the conventional AHP model can be better described by the integrated fuzzy AHP and QFD models.
Vorheriger Artikel Dual subspace learning via geodesic search on Stiefel manifold
Nächster Artikel An incremental approach to attribute reduction of dynamic set-valued information systems

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Metadaten
Titel
A model integrating fuzzy AHP with QFD for assessing technical factors in aviation safety
verfasst von
Chuen-Jyh Chen
Shih-Ming Yang
Shih-Chuan Chang
Publikationsdatum
01.10.2014
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 5/2014
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-013-0169-1

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