Algorithm for multiple attribute decision-making using T-spherical fuzzy Maclaurin symmetric mean operator

Document Type : Research Paper

Authors

1 School of Mathematics, Thapar Institute of Engineering and Technology, Deemed University, Patiala-147004, Punjab, India

2 Department of Mathematics, Riphah Institute of Computing and Applied Sciences (RICAS), Riphah International University (Lahore Campus), 54000, Lahore, Pakistan

3 Department of Mathematics and Statistics, International Islamic University Islamabad-44000, Pakistan

Abstract

The conception of T-spherical fuzzy set (TSFS) is a recent phenomenon in the environment of fuzzy notions that describe opinion about some certain objects under uncertainties with the help of four types of degrees known as the degree of membership (DM), degree of abstinence (DA), degree of non-membership (DNM) and degree of refusal (DR). The focus of this manuscript, to examine the interrelationship among any numbers of T-spherical fuzzy numbers (TSFNs) by the use Maclaurin symmetric mean (MSM) operators. We investigate and develop T-spherical fuzzy (TSF) MSM (TSFMSM) operator, TSF weighted MSM (TSFWMSM) operator, TSF dual MSM (TSFDMSM) operator, TSF weighted dual MSM (TSFWDMSM) operator. By using these investigated operators, some special cases of the explored operators are also developed, and their properties are examined. In addition, an algorithm for handling multi attribute decision making (MADM) problems based MSM operators in TSF setting. An illustrative example to check the applicability of the MSM operators of TSFSs is presented where the evaluation of technology commercialization is observed. To show the superiority of the newly established MSM operators, a comprehensive comparative study is designed numerically. In view of the comparative analysis, some advantages of the new MSM operators of the TSFNs are stated.

Keywords


[1] M. Akram, C. Kahraman, K. Zahid, Group decision-making based on complex spherical fuzzy VIKOR approach, Knowledge-Based Systems, 216 (2021), 106793.
[2] K. T. Atanassov, Intuitionistic fuzzy sets, Fuzzy Sets and Systems, 20(1) (1986), 87-96.
[3] K. T. Atanassov, Geometrical interpretation of the elements of the intuitionistic fuzzy objects, International Journal of Bio-Automation, 20 (2016), S27-S42.
[4] G. Beliakov, H. Bustince, D. P. Goswami, U. K. Mukherjee, N. R. Pal, On averaging operators for Atanassov’s intuitionistic fuzzy sets, Information Sciences, 181 (2011), 1116-1124.
[5] T. Y. Chen, Novel generalized distance measure of Pythagorean fuzzy sets and a compromise approach for multiple criteria decision analysis under uncertainty, IEEE Access, 7 (2019), 58168-58185.
[6] B. C. Cuong, Picture fuzzy sets, Journal of Computer Science and Cybernetics, 30(4) (2013), 409-420.
[7] D. W. Detemple, J. M. Robertson, On generalized symmetric means of two variables, Publikacije Elektrotehniaíkogfak ulteta, Serija Matematikaifizika, 634(677) (1979), 236-238.
[8] M. Deveci, L. Eriskin, M. Karatas, A survey on recent applications of Pythagorean fuzzy sets: A state-of-the-art between 2013 and 2020, in: H. Garg (Ed.), Pythagorean Fuzzy Sets: Theory and Applications, Springer, (2021), 3-38.
[9] H. Dinçer, S. Yuksel, A. Mikhaylov, S. E. Barykin, T. Aksoy, U. Hacioglu, Analysis of environmental priorities for green project investments using an integrated q-rung orthopair fuzzy modeling, IEEE Access, 10 (2022), 50996- 51007.
[10] P. A. Ejegwa, J. M. Agbetayo, Similarity-distance decision-making technique and its applications via intuitionistic fuzzy pairs, Journal of Computational and Cognitive Engineering, (2022), DOI:10.47852/
bonviewJCCE512522514.
[11] H. Garg, Some picture fuzzy aggregation operators and their applications to multicriteria decision-making, Arabian Journal for Science and Engineering, 42(12) (2017), 5275-5290.
[12] H. Garg, M. Munir, K. Ullah, T. Mahmood, N. Jan, Algorithm for T-spherical fuzzy multi-attribute decision making based on improved interactive aggregation operators, Symmetry, 10(12) (2018), 670.
[13] H. Garg, K. Ullah, T. Mahmood, N. Hassan, N. Jan, T-spherical fuzzy power aggregation operators and their applications in multi-attribute decision making, Journal of Ambient Intelligence and Humanized Computing, 12 (2021), 9067-9080.
[14] A. Guleria, R. K. Bajaj, T-spherical fuzzy soft sets and its aggregation operators with application in decision making, Scientia Iranica, 28(2) (2021), 1014-1029.
[15] Y. Ju, Y. Liang, C. Luo, P. Dong, E. D. Gonzalez, A. Wang, T-spherical fuzzy TODIM method for multi-criteria group decision-making problem with incomplete weight information, Soft Computing, 25(4) (2021), 2981-3001.
[16] F. Karaaslan, M. A. D. Dawood, Complex T-spherical fuzzy Dombi aggregation operators and their applications in multiple-criteria decision-making, Complex and Intelligent Systems, 7(5) (2021), 2711-2734.
[17] R. Khan, K. Ullah, D. Pamucar, M. Bari, Performance measure using a multi-attribute decision making approach based on complex T-spherical fuzzy power aggregation operators, Journal of Computational and Cognitive Engineering, 1(3) (2022), 138-146.
[18] M. Lin, X. Li, R. Chen, H. Fujita, J. Lin, Picture fuzzy interactional partitioned Heronian mean aggregation operators: An application to MADM process, Artificial Intelligence Review, 55(2) (2022), 1171-1208.
[19] P. Liu, Q. Khan, T. Mahmood, N. Hassan, T-spherical fuzzy power Muirhead mean operator based on novel operational laws and their application in multi-attribute group decision making, IEEE Access, 7 (2019), 22613- 22632.
[20] D. Liu, D. Peng, Z. Liu, The distance measures between q-rung orthopair hesitant fuzzy sets and their application in multiple criteria decisions making, International Journal of Intelligent Systems, 34(9) (2019), 2104-2121.
[21] Z. Liu, S. Wang, P. Liu, Multiple attribute group decision making based on q-rung orthopair fuzzy Heronian mean operators, International Journal of Intelligent Systems, 33(12) (2018), 2341-2363.
[22] C. Maclaurin, A second letter to Martin Folkes, Esq.; concerning the roots of equations, with demonstration of other rules of algebra, Philos Trans Roy Soc London Ser A, 36 (1729), 59-96.
[23] T. Mahmood, Z. Ali, Prioritized Muirhead mean aggregation operators under the complex single-valued neutrosophic settings and their application in multi-attribute decision making, Journal of Computational and Cognitive Engineering, 1(2) (2022), 56-73.
[24] T. Mahmood, K. Ullah, Q. Khan, N. Jan, An approach toward decision-making and medical diagnosis problems using the concept of spherical fuzzy sets, Neural Computing and Applications, 31(11) (2019), 7041-7053.
[25] E. Ozceylan, B. Ozkan, M. Kabak, M. Dagdeviren, A state-of-the-art survey on spherical fuzzy sets, Journal of Intelligent and Fuzzy Systems, 42(1) (2022), 195-212. [26] X. Peng, G. Selvachandran, Pythagorean fuzzy set: State of the art and future directions, Artificial Intelligence Review, 52 (2019), 1873-1927.
[27] X. Peng, Y. Yang, Some results for Pythagorean fuzzy sets, International Journal of Intelligent Systems, 30(11) (2015), 1133-1160.
[28] J. Qin, X. Liu, An approach to intuitionistic fuzzy multiple attribute decision making based on Maclaurin symmetric mean operators, Journal of Intelligent and Fuzzy Systems, 27(5) (2014), 2177-2190.
[29] M. Riyahi, A. Borumand Saeid, M. Kuchaki Rafsanjani. Improved q-rung orthopair and T-spherical fuzzy sets, Iranian Journal of Fuzzy Systems, 19(3) (2022), 155-170.
[30] M. R. Seikh, U. Mandal, Some picture fuzzy aggregation operators based on Frank t-norm and t-conorm: Application to MADM process, Informatica, 45(3) (2021), DOI:10.31449/inf.v45i3.3025.
[31] X. Shen, S. Sakhi, K. Ullah, M. N. Abid, Y. Jin. Information measures based on T-spherical fuzzy sets and their applications in decision making and pattern recognition, Axioms, 11(7) (2022), 302.
[32] S. Singh, A. H. Ganie. Some novel q-rung orthopair fuzzy correlation coefficients based on the statistical viewpoint with their applications, Journal of Ambient Intelligence and Humanized Computing, 13(4) (2022), 2227-2252.
[33] C. Tian, J. J. Peng, Z. Q. Zhang, J. Q. Wang, M. Goh, An extended picture fuzzy MULTIMOORA method based on Schweizer-Sklar aggregation operators, Soft Computing, (2022), 1-20.
[34] K. Ullah, Picture fuzzy maclaurin symmetric mean operators and their applications in solving multiattribute decision-making problems, Mathematical Problems in Engineering, 2021 (2021), DOI:10.1155/2021/1098631.
[35] K. Ullah, T. Mahmood, H. Garg, Evaluation of the performance of search and rescue robots using T-spherical fuzzy Hamacher aggregation operators, International Journal of Fuzzy Systems, 22(2) (2020), 570-582.
[36] G. Wei, M. Lu, Pythagorean fuzzy Maclaurin symmetric mean operators in multiple attribute decision making, International Journal of Intelligent Systems, 33(5) (2018), 1043-1070.
[37] G. Wei, C. Wei, J. Wang, H. Gao, Y. Wei, Some q-rung orthopair fuzzy Maclauring symmetric mean operators and their applications to potential evaluation of emerging technology commercialization, International Journal of Intelligent Systems, 34(1) (2019), 50-81.
[38] S. Xian, Y. Cheng, K. Chen, A novel weighted spatial T-spherical fuzzy C-means algorithms with bias correction for image segmentation, International Journal of Intelligent Systems, 37(2) (2022), 1239-1272.
[39] R. R. Yager, Pythagorean fuzzy subsets, In Joint IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS) IEEE, (2013), 57-61.
[40] R. R. Yager, Generalized orthopair fuzzy sets, IEEE Transactions on Fuzzy Systems, 25(5) (2016), 1222-1230.
[41] L. A. Zadeh, Fuzzy sets, Information and Control, 8 (1965), 338-353.