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An interactive consensus reaching model for decision making under hesitation linguistic environment. (English) Zbl 1366.91068

Summary: This paper aims to propose a new approach to complete the consensus reaching process for multiple attribute group decision making (MAGDM) where the experts’ preferences can be expressed by hesitant fuzzy linguistic term sets (HFLTSs). The possibility distribution based approach is used to deal with HFLTSs since it is a natural way to express the possible information contained in an HFLTS. Two kinds of consensus measures are defined: one is based on distances to the collective preference and the other is based on distances between experts. This paper focuses on the first consensus measure and correspondingly, an interactive consensus reachingmodel is developed. Finally, a numerical example is provided to show the computational steps of the proposed model. The example indicates that only two rounds of interactions are needed in the given case. By comparison with the existing model which was based on the second consensus measure, it is found that the first consensus measure is less restrictive than the second one. In general, the existing model has to conduct more rounds than the proposed one if the same consensus threshold is specified. The two models also differ in the identification rules.

MSC:

91B06 Decision theory

Software:

AFRYCA; FLINTSTONES
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References:

[1] Bouzarour-Amokrane, A bipolar consensus approach for group decision making problems, Expert Systems with Applications 42 pp 1759– (2015) · doi:10.1016/j.eswa.2014.09.061
[2] Chen, The reduction of interval type-2 LR fuzzy sets, IEEE Transactions on Fuzzy Systems 22 pp 840– (2014) · doi:10.1109/TFUZZ.2013.2277729
[3] Dong, Computing the numerical scale of the linguistic term set for the 2-tuple fuzzy linguistic representation model, IEEE Transactions on Fuzzy Systems 17 pp 1366– (2009) · doi:10.1109/TFUZZ.2009.2032172
[4] Dong, Consensus building in a local context for the AHP-GDM with the individual numerical scale and prioritization method, IEEE Transactions on Fuzzy Systems 23 pp 354– (2015) · doi:10.1109/TFUZZ.2014.2312974
[5] Dong, Consensus-based group decision making under multi-granular unbalanced2-tuple linguistic preference relations, Group Decision and Negotiation 24 pp 217– (2015) · doi:10.1007/s10726-014-9387-5
[6] Ertuğrul, Comparison of fuzzy AHP and fuzzy TOPSIS methods for facility location selection, The International Journal of Advanced Manufacturing Technology 39 pp 783– (2008) · doi:10.1007/s00170-007-1249-8
[7] Escobar, Some extensions of the precise consistency consensus matrix, Decision Support Systems 74 pp 67– (2015) · doi:10.1016/j.dss.2015.04.005
[8] Gong, Two consensus models based on the minimum cost and maximum return regarding either all individuals or one individual, European Journal of Operational Research 240 pp 183– (2015) · Zbl 1339.91039 · doi:10.1016/j.ejor.2014.06.035
[9] Farahani, Multiple criteria facility location problems: A survey, Applied Mathematical Modelling 34 pp 1689– (2010) · Zbl 1193.90143 · doi:10.1016/j.apm.2009.10.005
[10] Fu, An evidential reasoning based consensus model for multiple attribute group decision analysis problems with interval valued group consensus requirements, European Journal of Operational Research 223 pp 167– (2012) · Zbl 1253.91050 · doi:10.1016/j.ejor.2012.05.048
[11] He, Intuitionistic fuzzy multicriteria decision making with application to job hunting: A comparative perspective, Journal of Intelligent & Fuzzy Systems 30 pp 1935– (2016) · Zbl 1361.91017 · doi:10.3233/IFS-151904
[12] Herrera, A 2-tuple fuzzy linguistic representation model for computing with words, IEEE Transactions on Fuzzy Systems 8 pp 746– (2000) · doi:10.1109/91.890332
[13] Herrera-Viedma, A consensus support systems model for group decision making problems with multigranular linguistic preference relations, IEEE Transactions on Fuzzy Systems 13 pp 644– (2005) · Zbl 05452569 · doi:10.1109/TFUZZ.2005.856561
[14] Herrera-Viedma, A review of soft consensus models in a fuzzy environment, Information Fusion 17 pp 4– (2014) · doi:10.1016/j.inffus.2013.04.002
[15] Li, Multi-attribute decision making method considering the amount and reliability of intuitionistic fuzzy information, Journal of Intelligent & Fuzzy Systems 28 pp 1877– (2015)
[16] Liao, Distance and similarity measures for hesitant fuzzy linguistic term sets and their application in multi-criteria decision making, Information Sciences 271 pp 125– (2014) · Zbl 1341.68260 · doi:10.1016/j.ins.2014.02.125
[17] Liao, Some new hybrid weighted aggregation operators under hesitant fuzzy multi-criteria decision making environment, Journal of Intelligent & Fuzzy Systems 26 pp 1601– (2014)
[18] Liu, A fuzzy envelope for hesitant fuzzy linguistic term set and its application to multicriteria decision making, Information Sciences 258 pp 220– (2014) · Zbl 1320.68192 · doi:10.1016/j.ins.2013.07.027
[19] Martínez, An overview on the 2-tuple linguistic model for computing with words in decision making: Extensions, applications and challenges, Information Sciences 207 pp 1– (2012) · Zbl 06099570 · doi:10.1016/j.ins.2012.04.025
[20] Mendel, Simplified interval type-2 fuzzy logic systems, IEEE Transactions on Fuzzy Systems 21 pp 1056– (2013) · doi:10.1109/TFUZZ.2013.2241771
[21] Palomares, Consensus under a fuzzy context: Taxonomy, analysis framework AFRYCA and experimental case of study, Information Fusion 20 pp 252– (2014) · doi:10.1016/j.inffus.2014.03.002
[22] Palomares, A semisupervised multi-agent system model to support consensus reaching processes, IEEE Transactions on Fuzzy Systems 22 pp 762– (2014) · doi:10.1109/TFUZZ.2013.2272588
[23] Parreiras, Fuzzy set based consensus schemes for multicriteria group decision making applied to strategic planning, Group Decision and Negotiation 21 pp 153– (2012) · doi:10.1007/s10726-011-9231-0
[24] Pérez, A new consensus model for group decision making problems with nonhomogeneous experts, IEEE Transactions on Systems, Man, and Cybernetics: Systems 44 pp 494– (2014) · doi:10.1109/TSMC.2013.2259155
[25] Rodríguez, Hesitant fuzzy linguistic term sets for decision making, IEEE Transactions on Fuzzy Systems 20 pp 109– (2012) · doi:10.1109/TFUZZ.2011.2170076
[26] Rodríguez, Hesitant fuzzy sets: State of the art and future directions, International Journal of Intelligent Systems 29 pp 495– (2014) · doi:10.1002/int.21654
[27] Rodríguez, A position and perspective analysis of hesitant fuzzy sets on information fusion in decision making. Towards high quality progress, Information Fusion 29 pp 89– (2016) · doi:10.1016/j.inffus.2015.11.004
[28] Roselló, Using consensus and distances between generalized multiattribute linguistic assessments for group decision-making, Information Fusion 17 pp 83– (2014) · doi:10.1016/j.inffus.2011.09.001
[29] Torra, Hesitant fuzzy sets, International Journal of Intelligent Systems 25 pp 529– (2010) · Zbl 1198.03076
[30] Wang, A new version of 2-tuple fuzzy linguistic representation model for computing with words, IEEE Transactions on Fuzzy Systems 14 pp 435– (2006) · Zbl 05452612 · doi:10.1109/TFUZZ.2006.876337
[31] Wang, Multi-criteria decision-making methods based on the Hausdorff distance of hesitant fuzzy linguistic numbers, Soft Computing 20 pp 1621– (2016) · Zbl 06770002 · doi:10.1007/s00500-015-1609-5
[32] Wei, Operators and comparisons of hesitant fuzzy linguistic term sets, IEEE Transactions on Fuzzy Systems 22 pp 575– (2014) · doi:10.1109/TFUZZ.2013.2269144
[33] Wu, Possibility distribution based approach for magdm with hesitant fuzzy linguistic information, IEEE Transactions on Cybernetics 46 pp 694– (2016) · doi:10.1109/TCYB.2015.2413894
[34] Xu, A consensus based method for multi-criteria group decision making under uncertain linguistic setting, Group Decision and Negotiation 23 pp 127– (2014) · doi:10.1007/s10726-012-9310-x
[35] Yu, Mapping development of linguistic decision making studies, Journal of Intelligent & Fuzzy Systems 30 pp 2727– (2016) · Zbl 06720511 · doi:10.3233/IFS-152026
[36] Zadeh, Fuzzy logicąłąła personal perspective, Fuzzy Sets and Systems 281 pp 4– (2015) · Zbl 1368.03037 · doi:10.1016/j.fss.2015.05.009
[37] Zhang, On the use of multiplicative consistency in hesitant fuzzy linguistic preference relations, Knowledge-Based Systems 72 pp 13– (2014) · doi:10.1016/j.knosys.2014.08.026
[38] Zhang, Novel distance and similarity measures on hesitant fuzzy sets with applications to clustering analysis, Journal of Intelligent & Fuzzy Systems 28 pp 2279– (2015)
[39] Zhu, Consistency measures for hesitant fuzzy linguistic preference relations, IEEE Transactions on Fuzzy Systems 24 pp 72– (2014)
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