An improved failure mode and effect analysis method for multi-criteria group decision-making in green logistics risk assessment

•This study develops the K-means clustering method to partition the FMEA experts according to their evaluation.•A weight calculation method of FMEA model according to grey correlation coefficient and risk attitude is proposed.•The combination of RT-PROMETHEE II final ranking method is constructed.•A...

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Bibliographic Details
Published in:Reliability engineering & system safety Vol. 215; p. 107826
Main Authors: Liu, Peide, Li, Ying
Format: Journal Article
Language:English
Published: Barking Elsevier Ltd 01.11.2021
Elsevier BV
Subjects:
Adaptability
Attitudes
Cluster analysis
Clustering
cold chain green logistics risk assessment
Decision analysis
Decision making
Decision theory
Failure analysis
failure mode and effect analysis
Failure modes
K-means clustering method
Logistics
multi-criteria group decision-making
Multiple criterion
Reliability analysis
Reliability engineering
Risk assessment
RT-PROMETHEE II
Vector quantization
multi-criteria group decision-making
cold chain green logistics risk assessment
RT-PROMETHEE II
failure mode and effect analysis
K-means clustering method
ISSN:0951-8320, 1879-0836
Online Access:Get full text
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Summary:•This study develops the K-means clustering method to partition the FMEA experts according to their evaluation.•A weight calculation method of FMEA model according to grey correlation coefficient and risk attitude is proposed.•The combination of RT-PROMETHEE II final ranking method is constructed.•An extended regret theory based on HARA utility function is formed.•Two simulation experiments and comparisons are presented to prove the validity, reliability and superiority of the proposed FMEA model. As one of the powerful systematic reliability management tool, the failure mode and effect analysis (FMEA) method is adopted to investigate and analyze potential failure mode (FM) risks of the cold chain green logistics (CCGL). Since FMEA experts have different professional backgrounds and risk preferences, they may give evaluation from different angle and emphasis; and express different limited rational behavior. Therefore, this study proposes an improved FMEA model which considers the expert group and the bounded rational behavior of experts. Firstly, this paper develops the K-means clustering method to partition FMEA experts. Further, a calculation model of attribute weights that considers different risk attitudes is proposed. Additionally, this study proposes a combination final ranking method based on regret theory (RT) and PROMETHEE II method. A practical case and two simulation experiments are carried out to demonstrate the validity, reliability and superiority performance of the proposed FMEA model. The improved FMEA model can enhance the fairness and reasonableness of the expert evaluation, improve the adaptability and flexibility of FMEA, and enrich the theoretical research of FMEA method. Findings indicated that it is necessary to consider the grouping and risk attitudes of experts in the practical risk assessment.
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ISSN:0951-8320
1879-0836
DOI:10.1016/j.ress.2021.107826