Back to Search
Start Over
Fine-Kinney method based on fuzzy logic for natural gas pipeline project risk assessment.
- Source :
-
Soft Computing - A Fusion of Foundations, Methodologies & Applications . Nov2023, Vol. 27 Issue 22, p16465-16482. 18p. - Publication Year :
- 2023
-
Abstract
- Quality function deployment (QFD) method allows reviewing of customer requirements (CRs) and design requirements (DRs) simultaneously in order to handle the correlations and relationships of CRs and DRs in calculations. It has been often combined with fuzzy numbers, because it provides to gather the judgments of experts in vagueness environment more correctly and easily. This paper suggests employing q-rung orthopair fuzzy number (q-ROFN) for improving the fuzzy QFD approach. The proposed q-ROFN based QFD method uses q-ROFN to adjust the importance degrees of CRs based the relationships between CRs and DRs with the help of the correlations and relationships of CRs and DRs. q-ROFN presents more information than numbers such as intuitionistic fuzzy number and Pythagorean fuzzy number about the correlations and relationships of CRs and DRs. Furthermore, this work proposes integrating Fine-Kinney method and QFD method to cope with limitations of Fine-Kinney method. VIKOR (VIsekriterijumska optimizacija i KOm-promisno Resenje) approach based on q-ROFN is also used to rank DRs. The proposed integrated method provides a novel decision support model for the natural gas pipeline project (NGPP) risk assessment. There is no study about the integration of Fine-Kinney, q-ROFN-based QFD, VIKOR methods. The most significant DR and the second most important DR for NGPP risk assessment are electrical sparks and coating type factor, respectively. The results of this model present a road map to the managers of NGPP for an occupational health and safety policy by ordering the failure modes. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 14327643
- Volume :
- 27
- Issue :
- 22
- Database :
- Academic Search Index
- Journal :
- Soft Computing - A Fusion of Foundations, Methodologies & Applications
- Publication Type :
- Academic Journal
- Accession number :
- 172347826
- Full Text :
- https://doi.org/10.1007/s00500-023-09108-6