Affiliations: [a] School of Business, Central South University, Changsha, PR China | [b] Hunan Engineering Research Center for Intelligent Decision Making and Big Data on Industrial Development, Hunan University of Science and Technology, Xiangtan, China
| [c] School of Business, Hunan University of Science and Technology, Xiangtan, China
| [d] School of Business, Hunan University, Changsha, PR China
Correspondence:
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Corresponding author. Yi-Ting Wang, School of Business, Central South University, Changsha, PR China. E-mail: [email protected].
Abstract: Failure mode and effect analysis is a powerful risk analysis tool in engineering management. When properly conducted, FMEA can make a huge contribution to reduce costs. The traditional FMEA ranks the failure modes on the basis of Risk Priority Number, which is defined as the multiplication of three risk factors. However, this method has always been criticized for it can’t handle the situation where the information given is uncertain or ambiguous. In order to extend the application of FMEA under the fuzzy environment, in this paper, we proposed a novel risk assessment model known as probabilistic linguistic ELECTRE II method to rank failure modes based on FMEA. To realize this goal, probabilistic linguistic term sets (PLTSs) that consider both the hesitant information and probabilistic information are introduced to depict decision maker’s cognitive information. To better use the PLTSs in the decision-making process, some important information measures are defined, and a method to obtain the combined weight based on entropy weight of PLTSs is also proposed. Subsequently, we establish a score-deviation based PLTS-ELECTRE II model to study FMEA as a multi-criteria group decision-making problem. Finally, we successfully apply this model in a nuclear reheat valve system and the effectiveness of the proposed method is verified by sensitivity analysis and comparative analysis.
Keywords: Risk management, outranking, probabilistic linguistic term set, combined weight, hybrid distance measure