Prioritizing occupational safety risks with fuzzy FUCOM and fuzzy graph theory-matrix approach Bulanık FUCOM ve bulanık çizge teorisi-matris yaklaşımı ile iş güvenliği risklerinin önceliklendirilmesi


Golcuk I., Durmaz E. D., Şahin R.

Journal of the Faculty of Engineering and Architecture of Gazi University, cilt.38, sa.1, ss.57-69, 2023 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 38 Sayı: 1
  • Basım Tarihi: 2023
  • Doi Numarası: 10.17341/gazimmfd.970514
  • Dergi Adı: Journal of the Faculty of Engineering and Architecture of Gazi University
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Art Source, Compendex, TR DİZİN (ULAKBİM)
  • Sayfa Sayıları: ss.57-69
  • Anahtar Kelimeler: Failure mode and effects analysis, occupational safety risks, fuzzy logic, FUCOM, graph theory-matrix approach, MULTICRITERIA DECISION-MAKING, COGNITIVE MAPS, HEALTH, MODEL, SELECTION, WEIGHTS, FMEA, AHP
  • Gazi Üniversitesi Adresli: Evet

Özet

© 2023 Gazi Universitesi Muhendislik-Mimarlik. All rights reserved.In this study, a new failure mode and effects analysis (FMEA) model is proposed for evaluating occupational safety risks. In the classical FMEA, risk priority numbers (RPNs) are calculated by multiplying the risk scores of the occurrence, severity, and detectability. However, RPN numbers generated by classical FMEA have been the subject of severe criticism in the literature. To overcome the drawbacks of the classical FMEA, this study proposes a new Multiple Attribute Decision Making (MADM) model. The proposed risk evaluation model combines the full consistency method (FUCOM) and graph theory-matrix approach (GTMA) under a fuzzy environment. The risk scores of failure models and the weights of risk factors have been obtained using the fuzzy FUCOM method. On the other hand, the RPN value of each failure mode is calculated by utilizing fuzzy GTMA. Fuzzy GTMA considers all possible dependencies among risk factors, which in turn produces more accurate rankings. The fuzzy judgements of the decision makers are aggregated by using the least squares distance method. The proposed model is implemented in a real-life case study and the failure modes are ranked.