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.
StamatisD.H., Failure Mode And Effect Analysis: FMEA From Theory To Execution, Amer Society For Quality, New York, 2003.
2.
LiuH.C., FMEA Using Uncertainty Theories and MCDM Methods, Springer, Singapore, 2016.
3.
NieR.X., TianZ.P., WangX.K., WangJ.Q. and WangT.L., Risk evaluation by FMEA of supercritical water gasification system using multi-granular linguistic distribution assessment, Knowledge-Based Systems162 (2018), 185–201.
4.
GuoQ., ShengK.J., WangZ., ZhangX.L., YangH.Y. and MiaoR., Research on Element Importance of Shafting Installation Based on QFD and FMEA, Procedia Engineering174 (2017), 677–685.
5.
ArabsheybaniA., PaydarM.M. and SafaeiA.S., An integrated fuzzy MOORA method and FMEA technique for sustainable supplier selection considering quantity discounts and supplier’s risk, Journal of Cleaner Production190 (2018), 577–591.
6.
TianC., PengJ.J., ZhangW.Y., ZhangS. and WangJ.Q., Tourism environmental impact assessment based on improved AHP and picture fuzzy PROMETHEE II methods, Technological and Economic Development of Economy26 (2020), 1236–1261.
7.
CarpitellaS., CertaA., IzquierdoJ. and La FataC.M., A combined multi-criteria approach to support FMECA analyses: A real-world case, Reliability Engineering & System Safety169 (2018), 394–402.
8.
MohsenO. and FereshtehN., An extended VIKOR method based on entropy measure for the failure modes risk assessment – A case study of the geothermal power plant (GPP), Safety Science92 (2017), 160–172.
9.
HajiaghaS.H.R., HashemiS.S., MohammadiY. and ZavadskasE.K., Fuzzy belief structure based VIKOR method: An application for ranking delay causes of Tehran metro system by FMEA criteria, Transport31 (2016), 108–118.
10.
WangL.E., LiuH.C. and QuanM.Y., Evaluating the risk of failure modes with a hybrid MCDM model under interval-valued intuitionistic fuzzy environments, Computers & Industrial Engineering102 (2016), 175–185.
11.
HuangJ., LiZ.J. and LiuH.C., New approach for failure mode and effect analysis using linguistic distribution assessments and TODIM method, Reliability Engineering & System Safety167 (2017), 302–309.
12.
CertaA., EneaM., GalanteG.M. and La FataC.M., ELECTRE TRI-based approach to the failure modes classification on the basis of risk parameters: An alternative to the risk priority number, Computers & Industrial Engineering108 (2017), 100–110.
13.
DoumposM. and FigueiraJ.R., A multicriteria outranking approach for modeling corporate credit ratings: An application of the Electre Tri-nC method, Omega82 (2019), 166–180.
14.
RodríguezR.M., MartínezL. and HerreraF., Hesitant Fuzzy Linguistic Term Sets, IEEE Transactions on Fuzzy Systems20 (2012), 109–119.
15.
DeepakD., MathewB., JohnS. and GargH., A topological structure involving hesitant fuzzy sets, Journal of Intelligent & Fuzzy Systems36 (2019), 6401–6412.
16.
GrolleauJ. and TergnyJ., Manuel de référence du programme ELECTRE II, in: Pollution, SEMAMetra International. Direction Scientifique, 1971.
17.
XuZ.S., Deviation measures of linguistic preference relations in group decision making, Omega33 (2005), 249–254.
18.
XuZ.S., EOWA and EOWG operators for aggregating linguistic labels based on linguistic preference relations, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems12 (2004), 791–810.
19.
LiaoH.C., XuZ.S. and ZengX.J., Distance and similarity measures for hesitant fuzzy linguistic term sets and their application in multi-criteria decision making, Information Sciences271 (2014), 125–142.
20.
LiaoH.C., XuZ.S., ZengX.J. and MerigóJ.M., Qualitative decision making with correlation coefficients of hesitant fuzzy linguistic term sets, Knowledge-Based Systems76 (2015), 127–138.
21.
GargH. and KumarK., Linguistic interval-valued Atanassov intuitionistic fuzzy sets and their applications to group decision-making problems, IEEE Transactions on Fuzzy Systems27 (2019), 2302–2311.
22.
GouX.J., XuZ.S. and LiaoH.C., Multiple criteria decision making based on Bonferroni means with hesitant fuzzy linguistic information, Soft Computing21 (2017), 6515–6529.
23.
XuZ.S. and XiaM.M., Distance and similarity measures for hesitant fuzzy sets, Information Sciences181 (2011), 2128–2138.
24.
PangQ., WangH. and XuZ.S., Probabilistic linguistic term sets in multi-attribute group decision making, Information Sciences369 (2016), 128–143.
25.
LiJ. and WangJ.Q., An Extended QUALIFLEX Method Under Probability Hesitant Fuzzy Environment for Selecting Green Suppliers, International Journal of Fuzzy Systems19 (2017), 1866–1879.
26.
HellingerE., Neue Begründung der Theorie quadratischer Formen von unendlichvielen Veränderlichen, Journal Für Die Reine Und Angewandte Mathematik1909 (1909), 210–271.
27.
LourenzuttiR. and KrohlingR.A., The Hellinger distance in Multicriteria Decision Making: An illustration to the TOPSIS and TODIM methods, Expert Systems with Applications41 (2014), 4414–4421.
28.
Abu ArqubO., AL-SmadiM., MomaniS. and HayatT., Numerical solutions of fuzzy differential equations using reproducing kernel Hilbert space method, Soft Computing20 (2016), 3283–3302.
29.
GargH. and KaurG., A robust correlation coefficient for probabilistic dual hesitant fuzzy sets and its applications, Neural Computing and Applications DOI: 10.1007/s00521-019-04362-y (2019).
30.
GargH. and KaurG., Algorithm for Probabilistic Dual Hesitant Fuzzy Multi-Criteria Decision-Making Based on Aggregation Operators With New Distance Measures, Mathematics6 (2018), 280.
31.
GargH. and KumarK., Group decision making approach based on possibility degree measure under linguistic interval-valued intuitionistic fuzzy set environment, Journal of Industrial and Management Optimization DOI: 10.3934/JIMO.2018162 (2019).
32.
NieR.X. and WangJ.Q., Prospect theory-based consistency recovery strategies with multiplicative probabilistic linguistic preference relations in managing group decision making, Arabian Journal for Science and Engineering DOI: 10.1007/s13369-019-04053-9 (2019).
33.
TianZ.P., NieR.X. and WangJ.Q., Probabilistic linguistic multi-criteria decision-making based on evidential reasoning and combined ranking methods considering decision-makers’ psychological preferences, Journal of the Operational Research Society DOI: 10.1080/01605682.2019.1632752 (2019).
34.
WangL., WangX.-k., PengJ.-j. and WangJ.-q., The differences in hotel selection among various types of travellers: A comparative analysis with a useful bounded rationality behavioural decision support model, Tourism Management76 (2020), 103961.
35.
JinC., WangH. and XuZ.S., Uncertain Probabilistic Linguistic Term Sets in Group Decision Making, International Journal of Fuzzy Systems21 (2019), 1241–1258.
36.
GouX. and XuZ., Novel basic operational laws for linguistic terms, hesitant fuzzy linguistic term sets and probabilistic linguistic term sets, Information Sciences372 (2016), 407–427.
37.
WuX. and LiaoH., A consensus-based probabilistic linguistic gained and lost dominance score method, European Journal of Operational Research272 (2019), 1017–1027.
38.
BaiC., ZhangR., QianL. and WuY., Comparisons of probabilistic linguistic term sets for multi-criteria decision making, Knowledge-Based Systems119 (2017), 284–291.
39.
LiuP.D. and TengF., Some Muirhead mean operators for probabilistic linguistic term sets and their applications to multiple attribute decision-making, Applied Soft Computing68 (2018), 396–431.
40.
ShannonC.E.A., A mathematical theory of communication. AT&T Tech J, Acm Sigmobile Mobile Computing & Communications Review5 (2001), 3–55.
41.
HuangW.C., ShuaiB., SunY., WangY. and AntwiE., Using entropy-TOPSIS method to evaluate urban rail transit system operation performance: The China case, Transportation Research Part A Policy & Practice111 (2018), 292–303.
42.
LiaoH.C., YangL.Y. and XuZ.S., Two new approaches based on ELECTRE II to solve the multiple criteria decision making problems with hesitant fuzzy linguistic term sets, Applied Soft Computing63 (2018), 223–234.
43.
DucksteinL. and GershonM., Multicriterion analysis of a vegetation management problem using ELECTRE II, Appl. Math. Modelling7 (1983), 254–261.
44.
SongW.Y., MingX.G., WuZ.Y. and ZhuB.T., Failure modes and effects analysis using integrated weight-based fuzzy TOPSIS, International Journal of Computer Integrated Manufacturing26 (2013), 1172–1186.
45.
ChenN. and XuZ.S., Hesitant fuzzy ELECTRE II approach: A new way to handle multi-criteria decision making problems, Information Sciences292 (2015), 175–197.
46.
ShenK.W., LiL. and WangJ.Q., Circular economy model for recycling waste resources under government participation: A case study in industrial waste water circulation in China, Technological and Economic Development of Economy26(202), 21–47.
47.
TangM., LongY.L., LiaoH.C. and XuZ.S., Inclusion measures of probabilistic linguistic term sets and their application in classifying cities in the Economic Zone of Chengdu Plain, Applied Soft Computing82 (2019), 105572.
48.
ShakerF., ShahinA. and JahanyanS., Developing a two-phase QFD for improving FMEA: An integrative approach, International Journal of Quality & Reliability Management36 (2019), 1454–1474.
