An extended generalized TODIM for risk evaluation and prioritization of failure modes considering risk indicators interaction

Abstract

Failure Mode and Effect Analysis (FMEA) is considered as a proactive risk prevention and control technique that has been widely applied to identify, assess and eliminate the risk of failure modes in various fields. Nevertheless, the interactions between risk indicators and decision maker’s psychological behavior characteristics are seldom considered simultaneously in the current FMEA method. In this article, we develop a hybrid FMEA framework integrating generalized TODIM (an acronym in Portuguese of Interactive and Multi-criteria Decision Making) method, Choquet integral and Shapley index to remedy this gap. In the proposed FMEA framework, fuzzy measures and Shapley index are used to model the interaction relationships among risk indicators and to determine the weights of these indicators. The extended generalized TODIM method with fuzzy measure and Shapley index is presented to simulate the psychological behavior characteristics of FMEA team members. It is also applied to calculate the risk priority of each failure mode. Trapezoidal Fuzzy Numbers (TrFNs) are adopted to depict the uncertainty in the risk evaluation process. Furthermore, a new risk evaluation information fusion with TrFNs-WAIA (weighted arithmetic interaction averaging operator of the trapezoidal fuzzy numbers) operator based on $\lambda -$Shapley Choquet is developed to aggregate individual risk evaluation information of FMEA team member into a group risk evaluation matrix, which considers the potential correlations among these members. Finally, a practical example of FMEA problem is presented to demonstrate the application and feasibility of the proposed hybrid FMEA framework, and comparison and sensitivity studies are also conducted to validate the effectiveness of the improved FMEA approach.

Keywords:

• Risk assessment
• failure mode and effect analysis
• Choquet integral
• Shapley index
• TODIM

Acknowledgments

The authors are grateful to the Editors and three referees for comments and suggestions that were very helpful in improving the quality of the article. We are also grateful to Dr. Peng Rui for his useful suggestions.

Additional information

Funding

This work was supported by the National Science Foundation of China (NSFC) (71701158, 71771051, and 71371049), and MOE (Ministry of Education in China) Project of Humanities and Social Sciences (17YJC630114), and the Scientific Research Foundation of Graduate School of Southeast University (YBPY1876) and the Central Universities (2018IVB036 and 2019VI030), MOE Ministry of Education in China (MOE), Project of Humanities and Social Sciences (19YJC630160).

Notes on contributors

Weizhong Wang

Weizhong Wang received a M.Sc. degree in management science and engineering from China University of Mining and Technology, Xuzhou, in 2014. He is currently a Ph.D student in the School of Economics & Management, Southeast University. His research results have been published in IISE Transactions, Computers & industrial Engineering, Safety Science, International Journal of Industrial Ergonomics, among others. His current research interests include decision analysis, risk analysis, accident analysis.

Xinwang Liu

Xinwang Liu received M.Sc. and Ph.D. degrees in systems engineering from Southeast University, Nanjing, China, in 1996 and 1999, respectively. He is currently a professor with the Department of Management Science and Engineering, School of Economics and Management, Southeast University. He has authored or coauthored more than 100 publications of journals and international conferences. His research interests include type-2 fuzzy logic, aggregation operators, recommend systems, fuzzy decision making and safety management.

Jindong Qin

Jindong Qin received a M.Sc. degree in system engineering from Wuhan University of Technology, Wuhan, in 2012 and a Ph.D. degree in management science and engineering from Southeast University, Nanjing, China, in 2016, respectively. He is currently an associate professor in the school of management at Wuhan University of Technology. He currently serves as an associate editor of International Journal of Fuzzy Systems, International Journal of Computational Intelligence Systems and Granular Computing. His research results have been published in IISE Transactions, European Journal of Operational Research, Information Sciences, Knowledge-Based Systems, Applied Soft Computing, Soft Computing, among others. His current research interests include decision analysis, type-2 fuzzy theory, information fusion, computing with words and granular computing.

Shuli Liu

Shuli Liu received a Master’s degree in probability theory and mathematical statistics from Anhui University, Hefei, China, in 2006 and a Ph.D. in management science and engineering from Southeast University, Nanjing, China, in 2017, respectively. He is currently an associate professor in the school of management at Anhui Normal University. His current research interests include behavioral decision and behavioral economics.