Extension of hesitant fuzzy weight geometric (HFWG)-VIKOR method under hesitant fuzzy information

Authors

  • Rafiraza raza Faculty of Computer Science and Mathematics, University Malaysia Terengganu, Kuala Nerus, 210300, Malaysia
  • Ahmad Termimi Ab Ghani Faculty of Computer Science and Mathematics, University Malaysia Terengganu, Kuala Nerus, 210300, Malaysia
  • Lazim Abdullah Faculty of Computer Science and Mathematics, University Malaysia Terengganu, Kuala Nerus, 210300, Malaysia

Keywords:

Aggregation operator, VIKOR method, Hesitant fuzzy sets, HFWG operator

Abstract

The hesitant fuzzy set (HFS) is an innovative approach to decision-making under uncertainty. This work is primarily concerned with the HFS decision matrix’s aggregated operation. The introduction of induced VIKOR procedures, various extensions of HFSs aggregation operator, and essential approaches for multi-criteria decision-making (MCDM) are presented. This technique uses the aggregation operator, the HFWG operator, to rank alternatives and identify the compromise solution closest to the ideal solution. In this research work, the hesitant fuzzy weight geometric[1]VIKOR (HFWG-VIKOR) model is a novel technique to achieve this. By combining the hesitant fuzzy elements, the HFWG aggregation operator creates aggregated values expressed as a single value. As per the scope of our research work, MCDM under hesitant fuzzy sets with the HFWG[1]VIKOR method has been used, and their result revealed the best alternative is to find out. These results indicate good potential for objectives. The multi-criteria problem is then solved using the combined HFWG-VIKOR technique, and the outcomes are presented in an easy-to-understand way owing to aggregation operators. The application of the HFWG-VIKOR technique is finally illustrated using a numerical example.

Dimensions

L. A. Zadeh, “Fuzzy sets as a basis for a theory of possibility”, Fuzzy sets and systems 1 (1978) 3. https://www.sciencedirect.com/science/article/abs/pii/0165011478900295.

K. T. Atanassov, “More on intuitionistic fuzzy sets”, Fuzzy sets and systems 33 (1989) 37. https://doi.org/10.1016/0165-0114(89)90215-7.

K. T. Atanassov & K. T. Atanassov, Intuitionistic fuzzy sets, Springer, Physica-Verlag HD, 1999, pp. 1–137. https://doi.org/10.1007/978-3-7908-1870-3_1.

E. Szmidt & J. Kacprzyk, “Distances between intuitionistic fuzzy sets”, Fuzzy sets and systems 114 (2000) 505. https://www.sciencedirect.com/science/article/abs/pii/S0165011498002449.

F. Chiclana, F. Herrera & E. Herrera-Viedma,“Integrating multiplicative preference relations in a multipurpose decision-making model based on fuzzy preference relations”, Fuzzy sets and systems 122 (2001) 277.https://doi.org/10.1016/S0165-0114(00)00004-X.

P. Grzegorzewski,“Distances between intuitionistic fuzzy sets and/or interval-valued fuzzy sets based on the Hausdorff metric”, Fuzzy sets and systems 148 (2004) 319. https://doi.org/10.1016/j.fss.2003.08.005.

H. Mitchell,“A correlation coefficient for intuitionistic fuzzy sets”, International Journal of Intelligent Systems 19 (2004) 483. https://doi.org/10.1002/int.20004.

Z. Xu & R. R. Yager,“Some geometric aggregation operators based on intuitionistic fuzzy sets”, International Journal of General Systems 35 (2006) 417. https://doi.org/10.1080/03081070600574353.

G. Wei, X. Zhao & R. Lin,“Some induced aggregating operators with fuzzy number intuitionistic fuzzy information and their applications to group decision making”, International Journal of Computational Intelligence Systems 3 (2010) 84. https://doi.org/10.1080/18756891.2010.9727679.

Z. Pei & L. Zheng, “A novel approach to multi-attribute decision making based on intuitionistic fuzzy sets”, Expert Systems with Applications 39 (2012) 2560. https://doi.org/10.1016/j.eswa.2011.08.108.

V. Torra & Y. Narukawa, On hesitant fuzzy sets and decision, IEEE international conference on fuzzy systems, 2009, pp. 1378–1382. https://doi.org/10.1109/fuzzy.2009.5276884.

V. Torra, “Hesitant fuzzy sets”, International Journal of Intelligent Systems 25 (2010) 529. https://doi.org/10.1002/int.20418.

M. Xia & Z. Xu, ”Hesitant fuzzy information aggregation in decision making”, International Journal of Approximate Reasoning 52 (2011) 395. https://doi.org/10.1016/j.ijar.2010.09.002.

Z. Xu & M. Xia, “Distance and similarity measures for hesitant fuzzy sets”, Information sciences 181 (2011) 2128. https://doi.org/10.1016/j.ins.2011.01.028.

Z. Xu & M. Xia, “On distance and correlation measures of hesitant fuzzy information”, International Journal of Intelligent Systems 26 (2011) 410. https://doi.org/10.1002/int.20474.

M. Xia, Z. Xu & N. Chen, “Some hesitant fuzzy aggregation operators with their application in group decision making”, Group Decision and Negotiation 22 (2013) 259. https://doi.org/10.1007/s10726-011-9261-7.

F. Chiclana, F. Herrera & E. Herrera-Viedma, “Integrating three representation models in fuzzy multipurpose decision making based on fuzzy preference relations”, Fuzzy sets and Systems 97 (1998) 33. https://doi.org/10.1016/S0165-0114(96)00339-9.

W. Wang & X. Liu, “Some hesitant fuzzy geometric operators and their application to multiple attribute group decision making”, Technological and Economic Development of Economy 20 (2014) 371. https://doi.org/10.3846/20294913.2013.877094.

H. Zhu, J. Zhao & Y. Xu, “2-dimension linguistic computational model with 2-tuples for multi-attribute group decision making”, Knowledge Based Systems 103 (2016) 132. https://doi.org/10.1016/j.knosys.2016.04.006.

F. Jin, Z. Ni, H. Chen & Y. Li, “Approaches to group decision making with intuitionistic fuzzy preference relations based on multiplicative consistency”, Knowledge-Based Systems 97 (2016) 48. https://doi.org/10.1016/j.knosys.2016.01.017.

S. Zeng, J. M. Merigo, D. Palacios-Marques, H. Jin & F. Gu, “Intuitionistic fuzzy induced ordered weighted averaging distance oper ator and its application to decision making”, Journal of Intelligent & Fuzzy Systems 32 (2017) 11. https://content.iospress.com/articles/journal-of-intelligent-and-fuzzy-systems/ifs141219.

S. Zeng & Y. Xiao, “TOPSIS method for intuitionistic fuzzy multiple criteria decision making and its application to investment selection”, Kybernetes 45 (2016) 282. https://www.emerald.com/insight/content/doi/10.1108/K-04-2015-0093/full/html.

Z. Shouzhen, “An extension of OWAD operator and its application to uncertain multiple-attribute group decision-making”, Cybernetics and Systems 47 (2016) 363. http://dx.doi.org/10.1080/01969722.2016.1182362.

J. Qin, X. Liu & W. Pedrycz, “An extended VIKOR method based on prospect theory for multiple attribute decision making under interval type2 fuzzy environment”, Knowledge-Based Systems 86 (2015) 116. https://doi.org/10.1016/j.knosys.2015.05.025.

S. Zeng, W. Su & C. Zhang, “Intuitionistic fuzzy generalized probabilistic ordered weighted averaging operator and its application to group decision making”, Technological and Economic Development of Economy 22 (2016) 177. https://doi.org/10.3846/20294913.2014.984253.

L. Dymova, P. Sevastjanov & A. Tikhonenko, “An interval type-2 fuzzy extension of the TOPSIS method using alpha cuts”, Knowledge-Based Systems 83 (2015) 116. https://doi.org/10.1016/j.knosys.2015.03.014.

C. Zhu, L. Zhu & X. Zhang, “Linguistic hesitant fuzzy power aggregation operators and their applications in multiple attribute decision-making”, Information Sciences 367 (2016) 809. https://doi.org/10.1016/j.ins.2016.07.011.

J. J. Peng, J. Q. Wang, L. J. Yang & X. H. Chen, “An extension of ELECTRE to multi-criteria decision-making problems with multi-hesitant fuzzy sets”, Information Sciences 307 (2015) 113. https://www.sciencedirect.com/science/article/abs/pii/S0020025515001309.

R. R. Yager, “On ordered weighted averaging aggregation operators in multicriteria decision-making”, IEEE Transactions on systems, Man, and Cybernetics 18 (1988) 183. https://doi.org/10.1109/21.87068.

L. A. Zadeh,“A computational approach to fuzzy quantifiers in natural languages”, Computers & Mathematics with Applications 9 (1983) 149. https://doi.org/10.1016/B978-0-08-030253-9.50016-0.

R. R. Yager, “Quantifier guided aggregation using OWA operators”, International Journal of Intelligent Systems 11 (1996) 49. https://onlinelibrary.wiley.com/doi/abs/10.1002/(SICI)1098-111X(199601)11:1%3C49::AID-INT3%3E3.0.CO;2-Z.

T. Murofushi & M. Sugeno, “An interpretation of fuzzy measures and the Choquet integral as an integral with respect to a fuzzy measure”, Fuzzy sets and Systems 29 (1989) 201. https://doi.org/10.1016/0165-0114(89)90194-2.

M. Grabisch & C. Labreuche, “A decade of application of the Choquet and Sugeno integrals in multi-criteria decision aid”, Annals of Operations Research 175 (2010) 247.

https://doi.org/10.1007/s10479-009-0655-8.

C. Tan & X. Chen, “Intuitionistic fuzzy Choquet integral operator for multi-criteria decision making”, Expert Systems with Applications 37 (2010) 149. https://doi.org/10.1016/j.eswa.2009.05.005.

S. Opricovic & G.-H. Tzeng, “Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS”, European Journal of Operational Research 156 (2004) 445.

https://doi.org/10.1016/S0377-2217(03)00020-1.

M. K. Sayadi, M. Heydari & K. Shahanaghi, “Extension of VIKOR method for decision making problem with interval numbers”, Applied mathematical modelling 33 (2009) 2257.

https://doi.org/10.1016/j.apm.2008.06.002.

A. Sanayei, S. F. Mousavi & A. Yazdankhah, “Group decision making process for supplier selection with VIKOR under fuzzy environment”, Expert systems with applications 37 (2010) 24. https://doi.org/10.1016/j.eswa.2009.04.063.

L. Y. Chen & T.-C. Wang, “Optimizing partners choice in IS/IT outsourcing projects: The strategic decision of fuzzy VIKOR”, International Journal of production economics 120 (2009) 233. https://doi.org/10.1016/j.ijpe.2008.07.022.

B. Vahdani, et al., “Extension of VIKOR method based on interval-valued fuzzy sets”, The International Journal of Advanced Manufacturing Technology 47 (2010) 1231. https://doi.org/10.1007/s00170-009-2241-2.

J. R. San Cristobal, “Multi-criteria decision-making in the selection of a renewable energy project in spain: The Vikor method”, Renewable Energy 36 (2011) 498. https://doi.org/10.1016/j.renene.2010.07.031.

A. Sarkar, et al., “A hybrid approach based on dual hesitant q-rung orthopair fuzzy Frank power partitioned Heronian mean aggregation operators for estimating sustainable urban transport solutions”, Engineering Applications of Artificial Intelligence 124 (2023) 106505. https://doi.org/10.1016/j.engappai.2023.106505.

A. Adeel, M. Akram & N. C¸ agman, “Decision-making analysis based on hesitant fuzzy N-soft ELECTRE-I approach”, Soft Computing 26 (2022) 11849. https://doi.org/10.1007/s00500-022-06981-5.

M. Akram, G. Ali & J. C. R. Alcantud, “A new method of multi-attribute group decision making based on hesitant fuzzy soft expert information”, Expert Systems 40 (2023) e13357. https://doi.org/10.1111/exsy.13357.

M. Akram, A. Luqman & J. C. R. Alcantud, “An integrated ELECTREI approach for risk evaluation with hesitant Pythagorean fuzzy infor mation”, Expert Systems with Applications 200 (2022) 116945.

https://doi.org/10.1016/j.eswa.2022.116945.

M. Akram, A. Luqman & C. Kahraman, “Hesitant Pythagorean fuzzy ELECTRE-II method for multi-criteria decision-making problems”, Ap plied Soft Computing 108 (2021) 107479. https://doi.org/10.1016/j.asoc.2021.107479.

M. Akram, G. Muhiuddin & G. Santos-Garc´?a, “An enhanced VIKOR method for multi-criteria group decision-making with complex Fermatean fuzzy sets”, Mathematical Biosciences and Engineering 19 (2022) 7201. http://dx.doi.org/10.3934/mbe.2022340.

M. Akram, C. Kahraman & K. Zahid, “Group decision-making based on complex spherical fuzzy VIKOR approach”, Knowledge-Based Systems 216 (2021) 106793. https://doi.org/10.1016/j.knosys.2021.106793.

M. Akram, K. Zahid & C. Kahraman, “Integrated outranking techniques based on spherical fuzzy information for the digitalization of transportation system”, Applied Soft Computing 134 (2023) 109992. https://doi.org/10.1016/j.asoc.2023.109992.

M. Akram, et al., “An extended MARCOS method for MCGDM under 2-tuple linguistic q-rung picture fuzzy environment”, Engineering Applications of Artificial Intelligence 120 (2023) 105892. https://doi.org/10.1016/j.engappai.2023.105892.

L. A. Zadeh, “Fuzzy sets”, Information and control 8 (1965) 338.

https://doi.org/10.1016/S0019-9958(65)90241-X.

S. Opricovic, “Fuzzy VIKOR with an application to water resources planning”, Expert Systems with Applications 38 (2011) 12983. https://doi.org/10.1016/j.eswa.2011.04.097.

Mathematical step of VIKOR method under hesitant fuzzy sets.

Published

2024-09-27

How to Cite

Extension of hesitant fuzzy weight geometric (HFWG)-VIKOR method under hesitant fuzzy information. (2024). Journal of the Nigerian Society of Physical Sciences, 6(4), 2157. https://doi.org/10.46481/jnsps.2024.2157

Issue

Section

Computer Science

How to Cite

Extension of hesitant fuzzy weight geometric (HFWG)-VIKOR method under hesitant fuzzy information. (2024). Journal of the Nigerian Society of Physical Sciences, 6(4), 2157. https://doi.org/10.46481/jnsps.2024.2157