Towards the identification of MARCOS models based on intuitionistic fuzzy score functions
Bartłomiej Kizielewicz, Bartosz Paradowski, Jakub Więckowski, Wojciech Sałabun
DOI: http://dx.doi.org/10.15439/2022F249
Citation: Proceedings of the 17th Conference on Computer Science and Intelligence Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 30, pages 789–798 (2022)
Abstract. We encounter uncertainty in many areas. In decision-making, it is an aspect that allows for better modeling of real-world problems. However, many methods rely on crisp numbers in their calculations. It makes it necessary to use techniques that perform this conversion. In this paper, we address the problem of score functions assessment regarding their effectiveness and usefulness in the decision-making field. The selected methods were used to convert the intuitionistic fuzzy set matrix into crisp data, then used in the multi-criteria assessment. Managing the theoretical problem showed that the used techniques provide high similarity values. Moreover, they proved to be helpful when dealing with intuitionistic fuzzy sets in the decision-making area.
References
- X. Gandibleux, “Multiple criteria optimization: state of the art annotated bibliographic surveys,” 2006.
- C. C. Aggarwal and S. Y. Philip, “A survey of uncertain data algorithms and applications,” IEEE Transactions on knowledge and data engineering, vol. 21, no. 5, pp. 609–623, 2008.
- P. Ziemba, J. Jankowski, and J. Wątróbski, “Online comparison system with certain and uncertain criteria based on multi-criteria decision analysis method,” in International Conference on Computational Collective Intelligence. Springer, 2017, pp. 579–589.
- D. Dubois and H. Prade, “Membership functions,” in Fuzzy Approaches for Soft Computing and Approximate Reasoning: Theories and Applications. Springer, 2021, pp. 5–20.
- V. Torra, “Hesitant fuzzy sets,” International journal of intelligent systems, vol. 25, no. 6, pp. 529–539, 2010.
- T. Senapati and R. R. Yager, “Fermatean fuzzy sets,” Journal of Ambient Intelligence and Humanized Computing, vol. 11, no. 2, pp. 663–674, 2020.
- B. C. Cuong and V. Kreinovich, “Picture fuzzy sets,” Journal of Computer Science and Cybernetics, vol. 30, no. 4, pp. 409–420, 2014.
- P. Ejegwa, S. Akowe, P. Otene, and J. Ikyule, “An overview on intuitionistic fuzzy sets,” Int. J. Sci. Technol. Res, vol. 3, no. 3, pp. 142–145, 2014.
- M. J. Khan, M. I. Ali, P. Kumam, W. Kumam, M. Aslam, and J. C. R. Alcantud, “Improved generalized dissimilarity measure-based vikor method for pythagorean fuzzy sets,” International Journal of Intelligent Systems, vol. 37, no. 3, pp. 1807–1845, 2022.
- P. Thakur, B. Kizielewicz, N. Gandotra, A. Shekhovtsov, N. Saini, A. B. Saeid, and W. Sałabun, “A new entropy measurement for the analysis of uncertain data in mcda problems using intuitionistic fuzzy sets and copras method,” Axioms, vol. 10, no. 4, p. 335, 2021.
- S. Faizi, W. Sałabun, T. Rashid, S. Zafar, and J. Wątróbski, “Intuitionistic fuzzy sets in multi-criteria group decision making problems using the characteristic objects method,” Symmetry, vol. 12, no. 9, p. 1382, 2020.
- B. Gohain, R. Chutia, P. Dutta, and S. Gogoi, “Two new similarity measures for intuitionistic fuzzy sets and its various applications,” International Journal of Intelligent Systems, 2022.
- E. Szmidt, J. Kacprzyk, and P. Bujnowski, “Three term attribute description of atanassov’s intuitionistic fuzzy sets as a basis of attribute selection,” in 2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2021, pp. 1–6.
- N. X. Thao, “Some new entropies and divergence measures of intuitionistic fuzzy sets based on archimedean t-conorm and application in supplier selection,” Soft Computing, vol. 25, no. 7, pp. 5791–5805, 2021.
- S. Al-Humairi, A. Hizami, A. Zaidan, B. Zaidan, H. Alsattar, S. Qahtan, O. Albahri, M. Talal, A. Alamoodi, and R. Mohammed, “Towards sustainable transportation: A pavement strategy selection based on the extension of dual-hesitant fuzzy multi-criteria decision-making methods,” IEEE Transactions on Fuzzy Systems, 2022.
- S.-M. Chen and J.-M. Tan, “Handling multicriteria fuzzy decision-making problems based on vague set theory,” Fuzzy sets and systems, vol. 67, no. 2, pp. 163–172, 1994.
- T.-Y. Chen, “A comparative analysis of score functions for multiple criteria decision making in intuitionistic fuzzy settings,” Information Sciences, vol. 181, no. 17, pp. 3652–3676, 2011.
- S. K. De, R. Biswas, and A. R. Roy, “An application of intuitionistic fuzzy sets in medical diagnosis,” Fuzzy sets and Systems, vol. 117, no. 2, pp. 209–213, 2001.
- A. Kharal, “Homeopathic drug selection using intuitionistic fuzzy sets,” Homeopathy, vol. 98, no. 1, pp. 35–39, 2009.
- Ž. Stević, D. Pamučar, A. Puška, and P. Chatterjee, “Sustainable supplier selection in healthcare industries using a new mcdm method: Measurement of alternatives and ranking according to compromise solution (marcos),” Computers & Industrial Engineering, vol. 140, p. 106231, 2020.
- P. Ziemba, “Selection of electric vehicles for the needs of sustainable transport under conditions of uncertainty—a comparative study on fuzzy mcda methods,” Energies, vol. 14, no. 22, p. 7786, 2021.