Abstract
More and more often, we have to deal with uncertain data while making decisions. One popular way to model uncertain data is to use one of the many generalizations of fuzzy sets. In this paper, we would like to draw attention for the use of Hesitant fuzzy sets (HFSs) in solving decision-making problems. The main challenge is the complex algorithms that can guarantee high accuracy and operate on HFSs. The HFS COMET approach is known in the literature but is rarely used due to its complexity. The main contribution of our work is the simplification of the HFS COMET algorithm to make it more applicable. For this purpose, we make comparisons of different score functions, which are used to infer based on a hybrid algorithm that combines the advantages of TOPSIS and COMET methods. Finally, we have shown the efficiency of the proposed approach by using reference rankings and similarity coefficients.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Alcantud, J.C.R., Torra, V.: Decomposition theorems and extension principles for hesitant fuzzy sets. Inf. Fusion 41, 48–56 (2018)
Alcantud, J.C.R., de Andrés Calle, R., Torrecillas, M.J.M.: Hesitant fuzzy worth: an innovative ranking methodology for hesitant fuzzy subsets. Appl. Soft Comput. 38, 232–243 (2016)
Ali, J., Bashir, Z., Rashid, T.: Weighted interval-valued dual-hesitant fuzzy sets and its application in teaching quality assessment. Soft. Comput. 25(5), 3503–3530 (2020). https://doi.org/10.1007/s00500-020-05383-9
Faizi, S., Sałabun, W., Nawaz, S., ur Rehman, A., Wątróbski, J.: Best-worst method and hamacher aggregation operations for intuitionistic 2-tuple linguistic sets. Expert Syst. Appl. 181, 115088 (2021)
Farhadinia, B.: Distance and similarity measures for higher order hesitant fuzzy sets. Knowl.-Based Syst. 55, 43–48 (2014)
Farhadinia, B.: A series of score functions for hesitant fuzzy sets. Inf. Sci. 277, 102–110 (2014)
Gandotra, N., et al.: New pythagorean entropy measure with application in multi-criteria decision analysis. Entropy 23(12), 1600 (2021)
Kizielewicz, B., Sałabun, W.: A new approach to identifying a multi-criteria decision model based on stochastic optimization techniques. Symmetry 12(9), 1551 (2020)
Kizielewicz, B., Shekhovtsov, A., Sałabun, W.: Application of similarity measures for triangular fuzzy numbers in modified TOPSIS technique to handling data uncertainty. In: Kahraman, C., Cebi, S., Cevik Onar, S., Oztaysi, B., Tolga, A.C., Sari, I.U. (eds.) INFUS 2021. LNNS, vol. 307, pp. 409–416. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-85626-7_48
Kizielewicz, B., Shekhovtsov, A., Sałabun, W.: A new approach to eliminate rank reversal in the MCDA problems. In: Paszynski, M., Kranzlmüller, D., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds.) ICCS 2021. LNCS, vol. 12742, pp. 338–351. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-77961-0_29
Narayanamoorthy, S., Ramya, L., Baleanu, D., Kureethara, J.V., Annapoorani, V.: Application of normal wiggly dual hesitant fuzzy sets to site selection for hydrogen underground storage. Int. J. Hydrogen Energy 44(54), 28874–28892 (2019)
Sałabun, W., Urbaniak, K.: A new coefficient of rankings similarity in decision-making problems. In: Krzhizhanovskaya, V.V., et al. (eds.) ICCS 2020. LNCS, vol. 12138, pp. 632–645. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-50417-5_47
Sałabun, W., Wątróbski, J., Shekhovtsov, A.: Are MCDA methods benchmarkable? A comparative study of TOPSIS, VIKOR, COPRAS, and PROMETHEE II methods. Symmetry 12(9), 1549 (2020)
Sultan, A., Sałabun, W., Faizi, S., Ismail, M.: Hesitant fuzzy linear regression model for decision making. Symmetry 13(10), 1846 (2021)
Thakur, P., et al.: A new entropy measurement for the analysis of uncertain data in MCDA problems using intuitionistic fuzzy sets and COPRAS method. Axioms 10(4), 335 (2021)
Torra, V.: Hesitant fuzzy sets. Int. J. Intell. Syst. 25(6), 529–539 (2010)
Yang, M.S., Hussain, Z.: Distance and similarity measures of hesitant fuzzy sets based on Hausdorff metric with applications to multi-criteria decision making and clustering. Soft. Comput. 23(14), 5835–5848 (2019)
Zhu, B., Xu, Z., Xia, M.: Dual hesitant fuzzy sets. J. Appl. Math. 2012 (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Kizielewicz, B., Shekhovtsov, A., Sałabun, W. (2022). How to Make Decisions with Uncertainty Using Hesitant Fuzzy Sets?. In: Kahraman, C., Tolga, A.C., Cevik Onar, S., Cebi, S., Oztaysi, B., Sari, I.U. (eds) Intelligent and Fuzzy Systems. INFUS 2022. Lecture Notes in Networks and Systems, vol 505. Springer, Cham. https://doi.org/10.1007/978-3-031-09176-6_84
Download citation
DOI: https://doi.org/10.1007/978-3-031-09176-6_84
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-09175-9
Online ISBN: 978-3-031-09176-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)