An Intelligent Decision Support System for Spherical Fuzzy Sugeno-Weber Aggregation Operators and Real-Life Applications
DOI:
https://doi.org/10.31181/smeor11202415Keywords:
Spherical Fuzzy Information, Sugeno-Weber Aggregation Operators, Decision Support SystemAbstract
This article presents a novel approach to a decision support system for handling uncertainty and impreciseness in a large amount of human opinion. Sometimes, the aggregation of real-life applications is quite complex due to incomplete and redundant information about different preferences or alternatives. To handle such type of situations, a spherical fuzzy environment is a more effective and feasible framework with four components membership, abstinence, non-membership and refusal degree. We also formulate some flexible operations of Sugeno-Weber aggregation operators. Motivated by the theory of Sugeno-Weber t-norms, we constructed a family of mathematical methodologies, including Sugeno-Weber weighted average and weighted geometric operators in the light of spherical fuzzy information. An appropriate decision-making technique of the multi-attribute decision making (MADM) problem is also demonstrated to resolve complicated real-life applications. A numerical example is used to verify the compatibility and effectiveness of discussed mathematical approaches.
Downloads
References
Atanassov, K. T. (1986). Intuitionistic fuzzy sets. Fuzzy Sets and Systems, 20(1), 87–96. https://doi.org/10.1016/S0165-0114(86)80034-3
Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353. https://doi.org/10.1016/S0019-9958(65)90241-X
Garg, H., & Kumar, K. (2019). Multiattribute decision making based on power operators for linguistic intuitionistic fuzzy set using set pair analysis. Expert Systems, 36(4), e12428. https://doi.org/10.1111/exsy.12428
Garg, H. (2020). Linguistic Interval-Valued Pythagorean Fuzzy Sets and Their Application to Multiple Attribute Group Decision-making Process. Cognitive Computation, 12(6), 1313–1337. https://doi.org/10.1007/s12559-020-09750-4
Yager, R. R. (2016). Generalized orthopair fuzzy sets. IEEE Transactions on Fuzzy Systems, 25(5), 1222–1230. https://doi.org/10.1109/TFUZZ.2016.2604005
Cuong, B. (2015). Picture fuzzy sets. Journal of Computer Science and Cybernetics, 30. https://doi.org/10.15625/1813-9663/30/4/5032
Yager, R. R. (2013). Pythagorean fuzzy subsets. 2013 Joint IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS), 57–61. https://doi.org/10.1109/IFSA-NAFIPS.2013.6608375
Cuong, B. C. (2013). Picture fuzzy sets-first results. Part 1, seminar neuro-fuzzy systems with applications. Institute of Mathematics, Hanoi.
Atanassov, K., & Gargov, G. (1989). Interval valued intuitionistic fuzzy sets. Fuzzy Sets and Systems, 31(3), 343–349. https://doi.org/10.1016/0165-0114(89)90205-4
Cuong, B. (2015). Picture fuzzy sets. Journal of Computer Science and Cybernetics, 30. https://doi.org/10.15625/1813-9663/30/4/5032
Mahmood, T., Ullah, K., Khan, Q., & Jan, N. (2019). An approach toward decision-making and medical diagnosis problems using the concept of spherical fuzzy sets. Neural Computing and Applications, 31(11), 7041–7053. https://doi.org/10.1007/s00521-018-3521-2
Xu, Z. (2007). Intuitionistic fuzzy aggregation operators. IEEE Transactions on Fuzzy Systems, 15(6), 1179–1187. https://doi.org/10.1109/TFUZZ.2006.890678
Xu, Z., & Yager, R. R. (2006). Some geometric aggregation operators based on intuitionistic fuzzy sets. International Journal of General Systems, 35(4), 417–433. https://doi.org/10.1080/03081070600574353
Peng, X., & Yang, Y. (2015). Some Results for Pythagorean Fuzzy Sets: Some results for Pythagorean fuzzy sets. International Journal of Intelligent Systems, 30(11), 1133–1160. https://doi.org/10.1002/int.21738
Hussain, A., Ullah, K., Al-Quran, A., & Garg, H. (2023). Some T-spherical fuzzy dombi hamy mean operators and their applications to multi-criteria group decision-making process. Journal of Intelligent & Fuzzy Systems, 45(6), 9621–9641. https://doi.org/10.3233/JIFS-232505
Ren, P., Xu, Z., & Gou, X. (2016). Pythagorean fuzzy TODIM approach to multi-criteria decision making. Applied Soft Computing, 42, 246–259. https://doi.org/10.1016/j.asoc.2015.12.020
Garg, H. (2017a). Generalized Pythagorean fuzzy geometric aggregation operators using Einstein t-norm and t-conorm for multicriteria decision-making process. International Journal of Intelligent Systems, 32(6), 597–630. https://doi.org/10.1002/int.21860
Kaur, G., & Garg, H. (2019). Generalized cubic intuitionistic fuzzy aggregation operators using t-norm operations and their applications to group decision-making process. Arabian Journal for Science and Engineering, 44, 2775–2794. https://doi.org/10.1007/s13369-018-3532-4
Yang, L., & Li, B. (2020). Multiple-valued picture fuzzy linguistic set based on generalized heronian mean operators and their applications in multiple attribute decision making. IEEE Access, 8, 86272–86295. https://doi.org/10.1109/ACCESS.2020.2992434
Liu, P., & Wang, P. (2018). Some q-rung orthopair fuzzy aggregation operators and their applications to multiple-attribute decision making. International Journal of Intelligent Systems, 33(2), 259–280. https://doi.org/10.1002/int.21927
Yang, Z., Garg, H., Li, J., Srivastava, G., & Cao, Z. (2021). Investigation of multiple heterogeneous relationships using a q-rung orthopair fuzzy multi-criteria decision algorithm. Neural Computing and Applications, 33(17), 10771–10786. https://doi.org/10.1007/s00521-020-05003-5
Hussain, A., Ullah, K., Alshahrani, M. N., Yang, M.-S., & Pamucar, D. (2022). Novel Aczel–Alsina Operators for Pythagorean Fuzzy Sets with Application in Multi-Attribute Decision Making. Symmetry, 14(5), 940. https://doi.org/10.3390/sym14050940
Ali, Z., Mahmood, T., & Yang, M.-S. (2020). TOPSIS Method Based on Complex Spherical Fuzzy Sets with Bonferroni Mean Operators. Mathematics, 8(10), Article 10. https://doi.org/10.3390/math8101739
Dong, H., Ali, Z., Mahmood, T., & Liu, P. (2023). Power aggregation operators based on hamacher t-norm and t-conorm for complex intuitionistic fuzzy information and their application in decision-making problems. Journal of Intelligent & Fuzzy Systems, Preprint, 1–21. https://doi.org/10.3233/JIFS-230323
Hussain, A., Bari, M., & Javed, W. (2022). Performance of the Multi Attributed Decision-Making Process with Interval-Valued Spherical Fuzzy Dombi Aggregation Operators. Journal of Innovative Research in Mathematical and Computational Sciences, 1(1), 1-32.
Hussain, A., Latif, S., & Ullah, K. (2022). A Novel Approach of Picture Fuzzy Sets with Unknown Degree of Weights based on Schweizer-Sklar Aggregation Operators. Journal of Innovative Research in Mathematical and Computational Sciences, 1(2), 18-39.
Garg, H. (2017b). Some Picture Fuzzy Aggregation Operators and Their Applications to Multicriteria Decision-Making. Arabian Journal for Science and Engineering, 42(12), 5275–5290. https://doi.org/10.1007/s13369-017-2625-9
Wang, C., Zhou, X., Tu, H., & Tao, S. (2017). Some geometric aggregation operators based on picture fuzzy sets and their application in multiple attribute decision making. Italian journal of pure and applied mathematics, 37, 477–492.
Ullah, K., Hassan, N., Mahmood, T., Jan, N., & Hassan, M. (2019). Evaluation of Investment Policy Based on Multi-Attribute Decision-Making Using Interval Valued T-Spherical Fuzzy Aggregation Operators. Symmetry, 11(3), 357 https://doi.org/10.3390/sym11030357
Hussain, A., Ullah, K., Pamucar, D., Haleemzai, I., & Tatić, D. (2023). Assessment of Solar Panel Using Multiattribute Decision-Making Approach Based on Intuitionistic Fuzzy Aczel Alsina Heronian Mean Operator. International Journal of Intelligent Systems, 2023, e6268613. https://doi.org/10.1155/2023/6268613
Hussain, A., Ullah, K., Senapati, T., & Moslem, S. (2023). A robust decision-making approach for supplier selection using complex picture fuzzy information involving prioritization of attributes. IEEE Access, 11, 91807–91830. https://doi.org/10.1109/ACCESS.2023.3308030
Yager, R. R. (2001). The power average operator. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 31(6), 724–731. https://doi.org/10.1109/3468.983429
Xu, Z., & Yager, R. R. (2010). Intuitionistic fuzzy Bonferroni means. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 41(2), 568–578. https://doi.org/10.1109/TSMCB.2010.2072918
Wei, G., Zhao, X., Wang, H., & Lin, R. (2013). Fuzzy power aggregation operators and their application to multiple attribute group decision making. Technological and Economic Development of Economy, 19(3), 377–396. https://doi.org/10.3846/20294913.2013.821684
Wei, G., & Lu, M. (2018). Pythagorean fuzzy power aggregation operators in multiple attribute decision making. International Journal of Intelligent Systems, 33(1), 169–186. https://doi.org/10.1002/int.21946
Xu, Z. (2011). Approaches to multiple attribute group decision making based on intuitionistic fuzzy power aggregation operators. Knowledge-Based Systems, 24(6), 749–760. https://doi.org/10.1016/j.knosys.2011.01.011
Sugeno, M. (1974). Theory of fuzzy integrals and its applications. Doctoral Thesis, Tokyo Institute of Technology.
Weber, S. (1983). A general concept of fuzzy connectives, negations and implications based on t-norms and t-conorms. Fuzzy Sets and Systems, 11(1–3), 115–134. https://doi.org/10.1016/S0165-0114(83)80073-6
Ashraf, S., Iqbal, W., Ahmad, S., & Khan, F. (2023). Circular Spherical Fuzzy Sugeno Weber Aggregation Operators: A Novel Uncertain Approach for Adaption a Programming Language for Social Media Platform. IEEE Access. https://doi.org/10.1109/ACCESS.2023.3329242
Hwang, C.-M., Yang, M.-S., Hung, W.-L., & Lee, M.-G. (2012). A similarity measure of intuitionistic fuzzy sets based on the Sugeno integral with its application to pattern recognition. Information Sciences, 189, 93–109. https://doi.org/10.1016/j.ins.2011.11.029
Pamucar, D., Lazarević, D., Dobrodolac, M., Simic, V., & Görçün, Ö. F. (2024). Prioritization of crowdsourcing models for last-mile delivery using fuzzy Sugeno–Weber framework. Engineering Applications of Artificial Intelligence, 128, 107414. https://doi.org/10.1016/j.engappai.2023.107414
Sarkar, A., Senapati, T., Jin, L., Mesiar, R., Biswas, A., & Yager, R. R. (2023). Sugeno–Weber Triangular Norm-Based Aggregation Operators Under T-Spherical Fuzzy Hypersoft Context. Information Sciences, 119305. https://doi.org/10.1016/j.ins.2023.119305
Mahmood, T., Ullah, K., Khan, Q., & Jan, N. (2019). An approach toward decision-making and medical diagnosis problems using the concept of spherical fuzzy sets. Neural Computing and Applications, 31(11), 7041–7053. https://doi.org/10.1007/s00521-018-3521-2