A Novel Hesitant Cubical Dombi Fuzzy Aggregation Operators for Selecting Green Supplier Chain Managements

Authors

DOI:

https://doi.org/10.21015/vtm.v12i2.2006

Keywords:

Hesitant Fuzzy, Cubical Fuzzy Set, Hesitant Cubical Fuzzy Set, Dombi Aggregation Operators

Abstract

A hesitant fuzzy (HF) set enhances the concept of fuzzy sets by addressing disagreements among decision-makers about the membership degree of an element. Similarly, the Cubical Fuzzy Set (CFS) is useful for managing uncertainty in decision-making problems. However, existing methods often lack integration of hesitation and cubical uncertainty, and there is limited exploration of their combined effects on aggregation processes. In this paper, we introduce the Hesitant Cubical Fuzzy Set (HCFS), which integrates the principles of HF sets and CFS to address these limitations. We define several set-theoretical operations for HCFSs and develop Dombi operations for them. Furthermore, we present a range of aggregation operators based on Dombi operations, including the Hesitant Cubical Dombi Fuzzy Weighted Arithmetic Averaging (HCDFWAA) Operator, the Hesitant Cubical Dombi Fuzzy Weighted Geometric Averaging (HCDFWGA) Operator, the Hesitant Cubical Dombi Fuzzy Ordered Weighted Arithmetic Averaging (HCDFOWAA) Operator, and the Hesitant Cubical Dombi Fuzzy Ordered Weighted Geometric Averaging (HCDFOWGA) Operator, and examine their properties. Additionally, we propose a multi-criteria group decision-making method and algorithm within the Hesitant Cubical Fuzzy framework. To address gaps in practical application, we provide an example of the selection of green suppliers in supply chain management. We also perform a comparative analysis with existing operators to highlight the advantages and effectiveness of our approach, emphasizing how the integration of hesitation and cubical uncertainty can enhance decision-making processes.

References

Zadeh, L. A., 1965. Fuzzy sets. Information and control, 8(3), pp. 338-353. DOI: https://doi.org/10.1016/S0019-9958(65)90241-X

Gadekallu, T. R. and Gao, X. Z., 2021. An efficient attribute reduction and fuzzy logic classifier for heart disease and diabetes prediction. Recent Advances in Computer Science and Communications, 14(1), pp. 158-165. DOI: https://doi.org/10.2174/2213275911666181030124333

Sakthidasan, K., Gao, X. Z., Devabalaji, K. R. and Roopa, Y. M., 2021. Energy based random repeat trust computation approach and Reliable Fuzzy and Heuristic Ant Colony mechanism for improving QoS in WSN. Energy Reports, 7, pp. 7967-7976.

Atanassov, K. T. and Atanassov, K. T., 1999. Intuitionistic fuzzy sets. Physica-Verlag HD.

Yager, R. R., 2013. Pythagorean fuzzy subsets. In: 2013 joint IFSA world congress and NAFIPS annual meeting (IFSA/NAFIPS), IEEE, pp. 57-61.

Yager, R. R., 2013. Pythagorean membership grades in multicriteria decision making. IEEE Transactions on fuzzy systems, 22(4), pp. 958-965.

Cuong, B. C., 2013. Picture fuzzy sets-first results. Part 1, seminar neuro-fuzzy systems with applications. Institute of Mathematics, Hanoi.

Cuong, B. C., 2013. Picture fuzzy sets-first results. Part 1, seminar neuro-fuzzy systems with applications. Institute of Mathematics, Hanoi.

Garg, H., 2017. Some picture fuzzy aggregation operators and their applications to multicriteria decision-making. Arabian Journal for Science and Engineering, 42(12), pp. 5275-5290.

Peng, X. and Dai, J., 2017. Algorithm for picture fuzzy multiple attribute decision-making based on new distance measure. International Journal for Uncertainty Quantification, 7(2).

Wei, G., 2017. Some cosine similarity measures for picture fuzzy sets and their applications to strategic decision making. Informatica, 28(3), pp. 547-564.

Wei, G. and Gao, H., 2018. The generalized Dice similarity measures for picture fuzzy sets and their applications. Informatica, 29(1), pp. 107-124.

Wei, G., 2018. Some similarity measures for picture fuzzy sets and their applications. Iranian Journal of Fuzzy Systems, 15(1), pp. 77-89. DOI: https://doi.org/10.15388/Informatica.2018.160

Rafiq, M., Ashraf, S., Abdullah, S., Mahmood, T. and Muhammad, S., 2019. The cosine similarity measures of spherical fuzzy sets and their applications in decision making. Journal of Intelligent & Fuzzy Systems, 36(6), pp. 6059-6073.

Thao, N. X., 2020. Similarity measures of picture fuzzy sets based on entropy and their application in MCDM. Pattern analysis and applications, 23, pp. 1203-1213.

Singh, P., 2015. Correlation coefficients for picture fuzzy sets. Journal of Intelligent & Fuzzy Systems, 28(2), pp. 591-604.

Ganie, A. H., Singh, S. and Bhatia, P. K., 2020. Some new correlation coefficients of picture fuzzy sets with applications. Neural Computing and Applications, 32(16), pp. 12609-12625.

Son, L. H., 2016. Generalized picture distance measure and applications to picture fuzzy clustering. Applied Soft Computing, 46(C), pp. 284-295.

H~{A}a, A., 2016. Some improvements of fuzzy clustering algorithms using picture fuzzy sets and applications for geographic data clustering. VNU Journal of Science: Computer Science and Communication Engineering, 32(3).

Kutlu Gündou{g}du, F. and Kahraman, C., 2019. Spherical fuzzy sets and spherical fuzzy TOPSIS method. Journal of Intelligent & Fuzzy Systems, 36(1), pp. 337-352.

Kutlu Gündou{g}du, F. and Kahraman, C., 2020. Spherical fuzzy sets and decision making applications. In: Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making: Proceedings of the INFUS 2019 Conference, Istanbul, Turkey, July 23-25, 2019, Springer International Publishing, pp. 979-987.

Khan, A., Jan, A. U., Amin, F. and Zeb, A., 2022. Multiple attribute decision-making based on cubical fuzzy aggregation operators. Granular Computing, pp. 1-18.

Torra, V. and Narukawa, Y., 2009. On hesitant fuzzy sets and decision. In: 2009 IEEE International Conference on Fuzzy Systems, IEEE, pp. 1378-1382. DOI: https://doi.org/10.1109/FUZZY.2009.5276884

Torra, V., 2010. Hesitant fuzzy sets. International Journal of Intelligent Systems, 25(6), pp. 529-539. DOI: https://doi.org/10.1002/int.20418

Xu, Z. and Xia, M., 2011. On distance and correlation measures of hesitant fuzzy information. International Journal of Intelligent Systems, 26(5), pp. 410-425. DOI: https://doi.org/10.1002/int.20474

Li, D., Zeng, W. and Li, J., 2015. New distance and similarity measures on hesitant fuzzy sets and their applications in multiple criteria decision making. Engineering Applications of Artificial Intelligence, 40, pp. 11-16.

Xia, M., Xu, Z. and Chen, N., 2013. Some hesitant fuzzy aggregation operators with their application in group decision making. Group Decision and Negotiation, 22, pp. 259-279.

Chen, N., Xu, Z. and Xia, M., 2013. Interval-valued hesitant preference relations and their applications to group decision making. Knowledge-Based Systems, 37, pp. 528-540.

Peng, D. H., Wang, T. D., Gao, C. Y. and Wang, H., 2014. Continuous hesitant fuzzy aggregation operators and their application to decision making under interval-valued hesitant fuzzy setting. The Scientific World Journal, 2014(1), pp. 897304.

Mu, Z., Zeng, S. and Balev{z}entis, T., 2015. A novel aggregation principle for hesitant fuzzy elements. Knowledge-Based Systems, 84, pp. 134-143.

Amin, F., Fahmi, A., Abdullah, S., Ali, A., Ahmad, R. and Ghani, F., 2018. Triangular cubic linguistic hesitant fuzzy aggregation operators and their application in group decision making. Journal of Intelligent & Fuzzy Systems, 34(4), pp. 2401-2416.

Fahmi, A., Abdullah, S., Amin, F., Ali, A., Ahmed, R. and Shakeel, M., 2019. Trapezoidal cubic hesitant fuzzy aggregation operators and their application in group decision-making. Journal of Intelligent & Fuzzy Systems, 36(4), pp. 3619-3635.

Jiang, C., Jiang, S. and Chen, J., 2019. Interval-valued dual hesitant fuzzy Hamacher aggregation operators for multiple attribute decision making. Journal of Systems Science and Information, 7(3), pp. 227-256.

Liu, H. B., Liu, Y. and Xu, L., 2020. Dombi Interval-Valued Hesitant Fuzzy Aggregation Operators for Information Security Risk Assessment. Mathematical Problems in Engineering, 2020(1), p. 3198645.

Zeng, W., Xi, Y., Yin, Q. and Guo, P., 2021. Weighted dual hesitant fuzzy set and its application in group decision making. Neurocomputing, 458, pp. 714-726.

Wang, R. and Li, Y., 2018. Picture hesitant fuzzy set and its application to multiple criteria decision-making. Symmetry, 10(7), p. 295.

Liang, D., Darko, A. P., Xu, Z. and Wang, M., 2019. Aggregation of dual hesitant fuzzy heterogeneous related information with extended Bonferroni mean and its application to MULTIMOORA. Computers & Industrial Engineering, 135, pp. 156-176.

Liu, S., Hu, Y., Zhang, X., Li, Y. and Liu, L., 2020. Blockchain service provider selection based on an integrated BWM-entropy-TOPSIS method under an intuitionistic fuzzy environment. IEEE Access, 8, pp. 104148-104164.

Wei, G. W., 2010. GRA method for multiple attribute decision making with incomplete weight information in intuitionistic fuzzy setting. Knowledge-Based Systems, 23(3), pp. 243-247.

Khan, A., Jan, A. U., Amin, F. and Zeb, A., 2022. Multiple attribute decision-making based on cubical fuzzy aggregation operators. Granular Computing, pp. 1-18.

Yager, R. R., 1988. On ordered weighted averaging aggregation operators in multicriteria decisionmaking. IEEE Transactions on Systems, Man, and Cybernetics, 18(1), pp. 183-190. DOI: https://doi.org/10.1109/21.87068

Dombi, J., 1982. A general class of fuzzy operators, the DeMorgan class of fuzzy operators and fuzziness measures induced by fuzzy operators. Fuzzy Sets and Systems, 8(2), pp. 149-163. DOI: https://doi.org/10.1016/0165-0114(82)90005-7

Chen, X., Li, J., Qian, L. and Hu, X., 2016. Distance and similarity measures for intuitionistic hesitant fuzzy sets. In: 2016 International Conference on Artificial Intelligence: Technologies and Applications, Atlantis Press, pp. 182-186. DOI: https://doi.org/10.2991/icaita-16.2016.46

Garg, H., 2018. Hesitant Pythagorean fuzzy sets and their aggregation operators in multiple attribute decision-making. International Journal for Uncertainty Quantification, 8(3).

Yang, W. and Pang, Y., 2020. New q-rung orthopair hesitant fuzzy decision making based on linear programming and TOPSIS. IEEE Access, 8, pp. 221299-221311.

Downloads

Published

2024-09-30

How to Cite

Khan, A., & Zaman, T. (2024). A Novel Hesitant Cubical Dombi Fuzzy Aggregation Operators for Selecting Green Supplier Chain Managements. VFAST Transactions on Mathematics, 12(2), 67–98. https://doi.org/10.21015/vtm.v12i2.2006