Volume 23 , Issue 3 , PP: 154-174, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
C. Sivakumar 1 * , Mowafaq Omar Al-Qadri 2 , Abdallah shihadeh 3 , Ahmed Atallah Alsaraireh 4 , Abdallah Al-Husban 5 , P. Maragatha Meenakshi 6 , N. Rajesh 7 , M. Palanikumar 8
Doi: https://doi.org/10.54216/IJNS.230314
This paper introduces the concept of multiple attribute decision making (MADM) using q-rung square root interval valued neutrosophic sets (q-rung SRIVNS). The interval valued neutrosophic set (IVNS) and the q-rung square root neutrosophic set (q-rung SRNS) deals with the q-rung SRIVNS. The purpose of this article is to provide an analysis of several aggregating operations. In this article, we discuss a novel idea for the q-rung square root interval valued neutrosophic weighted averaging (q-rung SRIVNWA), q-rung ortho square root interval valued neutrosophic weighted geometric (q-rung SRIVNWG), generalized q-rung SRIVN weighted averaging (q-rung GSRIVNWA) and generalized q-rung SRIVN weighted geometric (q-rung GSRIVNWG). Using Euclidean distances and Hamming distances is illustrated with examples. These sets will be subjected to various algebraic operations in this communication. By doing this, models will be more accurate and will be closed to an integer q. The four most important factors for courier services in India are reliability, turnaround time, payment options, and tracking capabilities. Expert judgments and criteria will determine the most appropriate options. Furthermore, several proposed and current models are compared to demonstrate their reliability and utility. A fascinating and intriguing conclusion can be drawn from the study.
Aggregating operators , q-rung SRIVNWA , q-rung SRIVNWG , q-rung GSRIVNWA, q-rung GSRIVNWG.
[1] L. A. Zadeh, Fuzzy sets, Information and control, 8(3), (1965), 338-353.
[2] K. Atanassov, Intuitionistic fuzzy sets, Fuzzy sets and Systems, 20(1), (1986), 87-96.
[3] R. R. Yager, Pythagorean membership grades in multi criteria decision-making, IEEE Trans. Fuzzy Systems, 22, (2014), 958-965.
[4] S. Ashraf, S. Abdullah, T. Mahmood, F. Ghani and T. Mahmood, Spherical fuzzy sets and their applications in multi-attribute decision-making problems, Journal of Intelligent and Fuzzy Systems, 36, (2019), 2829-284.
[5] F. Smarandache, A unifying field in logics, Neutrosophy neutrosophic probability, set and logic, American Research Press, Rehoboth, (1999).
[6] P.A Ejegwa, J.M. Agbetayo, Similarity distance decision making technique and its applications via intuitionistic fuzzy pairs, Journal of Computational and Cognitive Engineering, 2023, 2(1) 1–7.
[7] B.C. Cuong and V. Kreinovich, Picture fuzzy sets a new concept for computational intelligence problems, in Proceedings of 2013 Third World Congress on Information and Communication Technologies (WICT 2013), IEEE, (2013), 1-6.
[8] W.F. Liu, J. Chang, X. He, Generalized Pythagorean fuzzy aggregation operators and applications in decision-making, Control Decis. 31, (2016), 2280-2286.
[9] X. Peng, and Y. Yang, Fundamental properties of interval valued Pythagorean fuzzy aggregation operators, International Journal of Intelligent Systems, (2015), 1-44.
[10] K.G. Fatmaa, K. Cengiza, Spherical fuzzy sets and spherical fuzzy TOPSIS method, Journal of Intelligent and Fuzzy Systems, 36(1), (2019), 337-352.
[11] P. Liu, G. Shahzadi, M. Akram, Specific types of q-rung picture fuzzy Yager aggregation operators for decision-making, International Journal of Computational Intelligence Systems, 13(1), (2020), 1072- 1091.
[12] T.M. Al-shami, H.Z. Ibrahim, A. A. Azzam , and A.I. EL-Maghrabi, square root-fuzzy sets and their weighted aggregated operators in application to decision-making, Journal of Function Spaces, 2022, 1- 14, 2022.
[13] R. Jansi, K. Mohana and F. Smarandache, Correlation measure for Pythagorean neutrosophic sets with T and Fas dependent neutrosophic components Neutrosophic Sets and Systems,30, (2019), 202-212.
[14] G. Shahzadi, M. Akram and A. B. Saeid, An application of single-valued neutrosophic sets in medical diagnosis, Neutrosophic Sets and Systems, 18, (2017), 80-88.
[15] P.A. Ejegwa, Distance and similarity measures for Pythagorean fuzzy sets, Granular Computing, (2018), 1-17.
[16] M. Palanikumar, K. Arulmozhi, and C. Jana, Multiple attribute decision-making approach for Pythagorean neutrosophic normal interval-valued aggregation operators, Comp. Appl. Math. 41(90), (2022), 1-27.
[17] M. Palanikumar, N. Kausar, S. F. Ahmed, S. A. Edalatpanah, E. Ozbilge, A. Bulut; New applications of various distance techniques to multi-criteria decision-making challenges for ranking vague sets, AIMS Mathematics, 8(5), 11397-11424.
[18] R.N. Xu and C.L. Li, Regression prediction for fuzzy time series, Appl. Math. J. Chinese Univ., 16, (2001), 451-461.
[19] M.S Yang, C.H. Ko, On a class of fuzzy c-numbers clustering procedures for fuzzy data, Fuzzy Sets and Systems, 84, (1996), 49-60.
[20] X. D. Peng and J. Dai, Approaches to single-valued neutrosophic MADM based on MABAC, TOPSIS and new similarity measure with score function, Neural Computing and Applications, 29(10), (2018), 939-954.
[21] X. Zhang and Z. Xu, Extension of TOPSIS to multiple criteria decision-making with Pythagorean fuzzy sets, International Journal of Intelligent Systems, 29, (2014), 1061-1078.