B. S. Ahn, Compatible weighting method with rank order centroid: Maximum entropy ordered
weighted averaging approach, Eur. J. Oper. Res., 212(3) (2011), 552–559.
 E. Akyar, H. Akyar and S. A. D¨uzce, Fuzzy risk analysis based on a geometric ranking method
for generalized trapezoidal fuzzy numbers, J. Intell. Fuzzy Syst., 25(1) (2013), 209–217.
 L. Aliahmadipour and E. Eslami, GHFHC: Generalized hesitant fuzzy hierarchical clustering
algorithm, Int. J. Intell. Syst., 31(9) (2016), 855–871.
 I. M. Aliev and Z. Kara, Fuzzy system reliability analysis using time dependent fuzzy set,
Control Cybern., 33(4) (2004), 653–662.
 S. Aytar, Order intervals in the metric space of fuzzy numbers, Iranian Journal of Fuzzy
Systems, 12(5) (2015), 139–147.
 A. S. A. Bakar and A. Gegov, Ranking of fuzzy numbers based on centroid point and spread,
J. Intell. Fuzzy Syst., 27(3) (2014), 1179–1186.
 S. J. Chen and S. M. Chen, Fuzzy risk analysis based on the ranking of generalized trapezoidal
fuzzy numbers, Appl. Intell., 26(1) (2007), 1–11.
 S. M. Chen and J. H. Chen, Fuzzy risk analysis based on ranking generalized fuzzy numbers
with different heights and different spreads, Expert. Syst. Appl., 36(3) (2009), 6833–6842.
 S. M. Chen, A. Munif, G. S. Chen, H. C. Liu and B. C. Kuo, Fuzzy risk analysis based on
ranking generalized fuzzy numbers with different left heights and right heights, Expert Syst.
Appl., 39(7) (2012), 6320 – 6334.
 S. M. Chen and K. Sanguansat, Analyzing fuzzy risk based on a new fuzzy ranking method
between generalized fuzzy numbers, Expert Syst. Appl., 38 (2011), 2163 – 2171.
 C. C. Chou, A generalized similarity measure for fuzzy numbers, J. Intell. Fuzzy Syst., 30(2)
 T. C. Chu and C. T. Tsao, Ranking fuzzy numbers with an area between the centroid point
and original point, Comput. Math. Appl., 43 (2002), 111–117.
 X. Deng, D. Han, J. Dezert, Y. Deng and Y. Shyr, Evidence combination from an evolutionary
game theory perspective, IEEE T. Cybernetics, 46(9) (2016), 2070–2082.
 X. Deng, W. Jiang and J. Zhang, Zero-sum matrix game with payoffs of Dempster-
Shafer belief structures and its applications on sensors, Sensors, Article ID 922,
DOI:10.3390/s17040922, 17(4) (2017), 1-22.
 X. Deng, F. Xiao and Y. Deng, An improved distance-based total uncertainty measure in
belief function theory, Appl. Intell., 46(4) (2017), 898–915.
 Y. Deng, Deng entropy, Chaos Soliton. Fract., 91 (2016), 549–553.
 D. S. Dinagar and A. Anbalagan, A new similarity measure between type-2 fuzzy numbers
and fuzzy risk analysis, Iranian Journal of Fuzzy Systems, 10(5) (2013), 79–95.
 A. Ebrahimnejad and J. L. Verdegay, An efficient computational approach for solving type-2
intuitionistic fuzzy numbers based transportation problems, Int. J. Comput. Int. Sys., 9(6)
 Y. B. Gong, L. L. Dai and N. Hu, Multi-attribute decision making method based on Bonferroni
mean operator and possibility degree of interval type-2 tarpezoidal fuzzy sets, Iranian Journal
of Fuzzy Systems, 13(5) (2016), 97–115.
 T. Hajjari, Fuzzy risk analysis based on ranking of fuzzy numbers via new magnitude method,
Iranian Journal of Fuzzy Systems, 12(3) (2015), 17–29.
 W. Jiang, S. Wang, X. Liu, H. Zheng and B. Wei, Evidence conflict measure based on owa
operator in open world, PloS one, 12(5) (2017), 1–18, e0177,828.
 W. Jiang, B. Wei, Y. Tang and D. Zhou, Ordered visibility graph average aggregation
operator: An application in produced water management, Chaos, Article ID 023,117,
DOI:10.1063/1.4977186, 27(2) (2017), 1-10.
 W. Jiang, B. Wei, J. Zhan, C. Xie and D. Zhou, A visibility graph power averaging
aggregation operator: A methodology based on network analysis, Comput. Ind. Eng.,
DOI:10.1016/j.cie.2016.09.009, 101 (2016), 260–268.
 W. Jiang and S.Wang, An uncertainty measure for interval-valued evidences, Int. J. Comput.
Commun., 12(5) (2017), 631–644.
 W. Jiang, C. Xie, M. Zhuang, Y. Shou and Y. Tang, Sensor data fusion with Z-numbers and
its application in fault diagnosis, Sensors, Article ID 1509, DOI:10.3390/s16091509, 16(9)
 W. Jiang, C. Xie, M. Zhuang and Y. Tang, Failure mode and effects analysis
based on a novel fuzzy evidential method, Appl.Soft Comput., DOI:
http://dx.doi.org/doi:10.1016/j.asoc.2017.04.008, 57 (2017), 672–683,
 W. Jiang and J. Zhan, A modified combination rule in generalized evidence theory, Appl.
Intell., DOI:10.1007/s10489-016-0851-6, 46(3) (2017), 630–640.
 B. Kang, Y. Hu, Y. Deng and D. Zhou, A New Methodology of Multicriteria Decision-Making
in Supplier Selection Based on Z-Numbers, Math. Probl. Eng., DOI:10.1155/2016/8475987,
2016(1) (2016), 1-17.
 J. Kerr-Wilson and W. Pedrycz, Some new qualitative insights into quality of fuzzy rule-based
models, Fuzzy Set. Syst., 307 (2017), 29–49.
 L. Kovarova and R. Viertl, The generation of fuzzy sets and the construction of characterizing
functions of fuzzy data, Iranian Journal of Fuzzy Systems, 12(6) (2015), 1–16.
 K. U. Madhuri, S. S. Babu and N. R. Shankar, Fuzzy risk analysis based on the novel fuzzy
ranking with new arithmetic operations of linguistic fuzzy numbers, J. Intell. Fuzzy Syst.,
26(5) (2014), 2391–2401.
 A. M. Nejad and M. Mashinchi, Ranking fuzzy numbers based on the areas on the left and
the right sides of fuzzy number, Comput. Math. Appl., 61(2) (2011), 431–442.
 M. T. Nouei, A. V. Kamyad, M. R. Sarzaeem and S. Ghazalbash, Fuzzy risk assessment of
mortality after coronary surgery using combination of adaptive neuro-fuzzy inference system
and K-means clustering, Expert Syst., 33(3) (2016), 230–238.
 M. O’Hagan, Aggregating template or rule antecedents in real-time expert systems with fuzzy
set logic, In: in proc. 22nd Annu. IEEE Asilomar Conf. Signals, Systems, Computers, Pacific
Grove, CA, 2(2) (1988), 681–689.
 G. Shafer, A Mathematical Theory of Evidence, Princeton University Press, 20(1) (1976),
 M. Shamsizadeh and M. M. Zahedi, Intuitionistic general fuzzy automata, Soft Comput.,
20(9) (2016), 3505–3519.
 G. P. Silveira and L. C. D. Barros, Analysis of the dengue risk by means of a Takagi-Sugenostyle
model, Fuzzy Set. Syst., 277(C) (2015), 122–137.
 E. B. Smith and R. Langari, Fuzzy multiobjective decision making for navigation of mobile
robots in dynamic, unstructured environments, J. Intell. Fuzzy Syst., 14(2) (2003), 95–108.
 J. Wang, Y. Hu, F. Xiao, X. Deng and Y. Deng, A novel method to use fuzzy soft sets in
decision making based on ambiguity measure and Dempster-Shafer theory of evidence: An
application in medical diagnosis, Artif. Intell. Med., 69 (2016), 1–11.
 Y. J. Wang, Ranking triangle and trapezoidal fuzzy numbers based on the relative preference
relation, Appl. Math. Model., 39(2) (2014), 586–599.
 Y. M. Wang, J. B. Yang, D. L. Xu and K. S. Chin, On the centroids of fuzzy numbers, Fuzzy
Set. Syst., 157(7) (2006), 919–926.
 S. H.Wei and S. M. Chen, Fuzzy risk analysis based on interval-valued fuzzy numbers, Expert.
Syst. Appl., 36(2) (2009), 2285–2299.
 D. Wu, X. Liu, F. Xue, H. Zheng, Y. Shou and W. Jiang, A new medical diagnosis method
based on Z-numbers, Appl. Intell., DOI:10.1007/s10489-017-1002-4, 2017(1) (2017), 1–14.
 R. R. Yager, Ranking fuzzy subsets over the unit interval, IEEE Conference on Decision and
Control including the 17th Symposium on Adaptive Processes, (1978), 1435–1437.
 R. R. Yager, On ordered weighted averaging aggregation operators in multicriteria decisionmaking,
IEEE T. Syst. Man. CY-S., 18(1) (1988), 183–190.
 V. F. Yu, H. T. X. Chi, L. Q. Dat, P. N. K. Phuc and C. W. Shen, Ranking generalized fuzzy
numbers in fuzzy decision making based on the left and right transfer coefficients and areas,
Appl. Math. Model., 37(16-17) (2013), 8106–8117.
 L. A. Zadeh, Fuzzy sets, Information and Control, 8 (1965), 338–353.
 L. A. Zadeh, A note on Z-numbers, Inform. Sciences, 181(14) (2011), 2923–2932.
 R. Zhang, X. Ran, C. Wang and Y. Deng, Fuzzy evaluation of network vulnerability, Qual.
Reliab. Eng. Int., 32(5) (2016), 1715–1730.
 X. Zhang, Y. Deng, F. T. S. Chan, A. Adamatzky and S. Mahadevan, Supplier selection
based on evidence theory and analytic network process, P. I. Mech. Eng. B-J. Eng., 230(3)