Research Article
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Year 2023, , 106 - 121, 21.06.2023
https://doi.org/10.38088/jise.1162151

Abstract

References

  • Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 3(8): 338-353.
  • Atanassov, K. T. (1983). Intuitionistic Fuzzy Sets. VII ITKR Session, Sofia.
  • Smarandache, F. (2005). Neutrosophic set-a generalization of the intuitionistic fuzzy set. International Journal of Pure and Applied Mathematics, 3(24): 287.
  • Yager, R. R. (2013). Pythagorean fuzzy subsets. 2013 Joint IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS).
  • Yager, R. R. (2016). Generalized orthopair fuzzy sets. IEEE Transactions on Fuzzy Systems, 25(5): 1222-1230.
  • Senapati, T. and Yager, R. R. (2020). Fermatean fuzzy sets. Journal of Ambient Intelligence and Humanized Computing, 11: 663-674.
  • Cuong, B. C. and Kreinovich, V. (2014). Picture fuzzy sets. Journal of Computer Science and Cybernetics, 30(4): 409-420.
  • Mahmood, T., Ullah, K., Khan, Q. and Jan, N. (2018). An approach toward decision-making and medical diagnosis problems using the concept of spherical fuzzy sets. Neural Computing and Applications, 31: 7043-7053.
  • Kaya, İ., Çolak, M. and Terzi, F. (2019). A comprehensive review of fuzzy multi criteria decision making methodologies for energy policy making. Energy Strategy Reviews, 24: 207-228.
  • Mardani, A., Nilashi, M., Zavadskas, E. K., Awang, S. R., Zare, H. and Jamal, N. M. (2018). Decision making methods based on fuzzy aggregation operators: Three decades review from 1986 to 2017. International Journal of Information Technology & Decision Making, 17(2): 391-466.
  • Salih, M. M., Zaidan, B. B., Zaidan, A. A. and Ahmed, M. A. (2019). Survey on fuzzy TOPSIS state-of-the-art between 2007 and 2017. Computers and Operations Research, 104: 207-227.
  • Alkan, N. and Kahraman, C. (2021). Evaluation of government strategies against COVID-19 pandemic using q-rung orthopair fuzzy TOPSIS method. Applied Soft Computing, 110: 107653.
  • Kahraman, C., Oztaysi, B., Otay, I. and Onar, S. C. (2020). Extensions of ordinary fuzzy sets: a comparative literature review. International Conference on Intelligent and Fuzzy Systems.
  • Sevastjanov, P. and Dymova, L. (2021). On the neutrosophic, pythagorean and some other novel fuzzy sets theories used in decision making: invitation to discuss. Entropy, 23: 1485.
  • Akram, M. and Dudek, W. A. (2011). Interval-valued fuzzy graphs. Computers and Mathematics with Applications, 61: 289-299.
  • Smarandache, F. (2014). Introduction to neutrosophic statistics, Infinite Study.
  • Işık, G. and Kaya, İ. (2022). A new integrated methodology for constructing linguistic pythagorean fuzzy statements for decision making problems. Journal of Intelligent & Fuzzy Systems, 43(4): 4883-4894.
  • Xu, Z. (2007). Intuitionistic fuzzy aggregation operators. IEEE Transactions on fuzzy systems, 15(6): 1179-1187.
  • Şahin, R. and Küçük, A. (2014). Generalised neutrosophic soft set and its integration to decision making problem. Applied Mathematics & Information, 8( 6): 2751-2759.
  • Işık, G. and Kaya, İ. (2021). Design and analysis of acceptance sampling plans based on intuitionistic fuzzy linguistic terms. Iranian Journal of Fuzzy Systems, 18(6): 101-118.

Fuzzy Modeling of non-MCDM Problems Under Indeterminacy

Year 2023, , 106 - 121, 21.06.2023
https://doi.org/10.38088/jise.1162151

Abstract

Fuzzy set theory (FST) is a popular approach for modeling the uncertainties of real-life problems. In some cases, uncertainty level of the events may not be determined surely because of some environmental factors. There are various FST extensions in the literature that consider such indeterminacy cases in modeling. Since some parts of the theories of FST extensions overlap with some others, the theories and the nature of considered scenarios must be understood well to obtain reliable results. Nevertheless, most of the studies in the literature do not conceptually analyze the nature of the uncertainty and decides an FST extension as a pre-step of the study without expressing an apparent reason. Therefore, the quality of the obtained results becomes questionable. Most of the FST extensions have been developed in line with the requirements of Multi-Criteria Decision-Making (MCDM) problem thus assumptions and limitations of these theories can cause reliability issues for the fuzzy models of the problems different from MCDM. In the scope of this study, capabilities, advantages, and disadvantages of well-known FST extensions that consider indeterminacy are conceptually analyzed and compared in line with the needs of modeling of the continuous systems, MCDM problems, and different problems from MCDM. The analysis has also been illustrated on numerical examples to make findings clear. The analysis showed that some extensions have clear advantages over others for specific scenarios. This study is an invitation to fulfill the gap in the field of fuzzy modeling of the different problems from MCDM.

References

  • Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 3(8): 338-353.
  • Atanassov, K. T. (1983). Intuitionistic Fuzzy Sets. VII ITKR Session, Sofia.
  • Smarandache, F. (2005). Neutrosophic set-a generalization of the intuitionistic fuzzy set. International Journal of Pure and Applied Mathematics, 3(24): 287.
  • Yager, R. R. (2013). Pythagorean fuzzy subsets. 2013 Joint IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS).
  • Yager, R. R. (2016). Generalized orthopair fuzzy sets. IEEE Transactions on Fuzzy Systems, 25(5): 1222-1230.
  • Senapati, T. and Yager, R. R. (2020). Fermatean fuzzy sets. Journal of Ambient Intelligence and Humanized Computing, 11: 663-674.
  • Cuong, B. C. and Kreinovich, V. (2014). Picture fuzzy sets. Journal of Computer Science and Cybernetics, 30(4): 409-420.
  • Mahmood, T., Ullah, K., Khan, Q. and Jan, N. (2018). An approach toward decision-making and medical diagnosis problems using the concept of spherical fuzzy sets. Neural Computing and Applications, 31: 7043-7053.
  • Kaya, İ., Çolak, M. and Terzi, F. (2019). A comprehensive review of fuzzy multi criteria decision making methodologies for energy policy making. Energy Strategy Reviews, 24: 207-228.
  • Mardani, A., Nilashi, M., Zavadskas, E. K., Awang, S. R., Zare, H. and Jamal, N. M. (2018). Decision making methods based on fuzzy aggregation operators: Three decades review from 1986 to 2017. International Journal of Information Technology & Decision Making, 17(2): 391-466.
  • Salih, M. M., Zaidan, B. B., Zaidan, A. A. and Ahmed, M. A. (2019). Survey on fuzzy TOPSIS state-of-the-art between 2007 and 2017. Computers and Operations Research, 104: 207-227.
  • Alkan, N. and Kahraman, C. (2021). Evaluation of government strategies against COVID-19 pandemic using q-rung orthopair fuzzy TOPSIS method. Applied Soft Computing, 110: 107653.
  • Kahraman, C., Oztaysi, B., Otay, I. and Onar, S. C. (2020). Extensions of ordinary fuzzy sets: a comparative literature review. International Conference on Intelligent and Fuzzy Systems.
  • Sevastjanov, P. and Dymova, L. (2021). On the neutrosophic, pythagorean and some other novel fuzzy sets theories used in decision making: invitation to discuss. Entropy, 23: 1485.
  • Akram, M. and Dudek, W. A. (2011). Interval-valued fuzzy graphs. Computers and Mathematics with Applications, 61: 289-299.
  • Smarandache, F. (2014). Introduction to neutrosophic statistics, Infinite Study.
  • Işık, G. and Kaya, İ. (2022). A new integrated methodology for constructing linguistic pythagorean fuzzy statements for decision making problems. Journal of Intelligent & Fuzzy Systems, 43(4): 4883-4894.
  • Xu, Z. (2007). Intuitionistic fuzzy aggregation operators. IEEE Transactions on fuzzy systems, 15(6): 1179-1187.
  • Şahin, R. and Küçük, A. (2014). Generalised neutrosophic soft set and its integration to decision making problem. Applied Mathematics & Information, 8( 6): 2751-2759.
  • Işık, G. and Kaya, İ. (2021). Design and analysis of acceptance sampling plans based on intuitionistic fuzzy linguistic terms. Iranian Journal of Fuzzy Systems, 18(6): 101-118.
There are 20 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Research Articles
Authors

Gürkan Işık 0000-0002-5297-3109

Early Pub Date June 20, 2023
Publication Date June 21, 2023
Published in Issue Year 2023

Cite

APA Işık, G. (2023). Fuzzy Modeling of non-MCDM Problems Under Indeterminacy. Journal of Innovative Science and Engineering, 7(1), 106-121. https://doi.org/10.38088/jise.1162151
AMA Işık G. Fuzzy Modeling of non-MCDM Problems Under Indeterminacy. JISE. June 2023;7(1):106-121. doi:10.38088/jise.1162151
Chicago Işık, Gürkan. “Fuzzy Modeling of Non-MCDM Problems Under Indeterminacy”. Journal of Innovative Science and Engineering 7, no. 1 (June 2023): 106-21. https://doi.org/10.38088/jise.1162151.
EndNote Işık G (June 1, 2023) Fuzzy Modeling of non-MCDM Problems Under Indeterminacy. Journal of Innovative Science and Engineering 7 1 106–121.
IEEE G. Işık, “Fuzzy Modeling of non-MCDM Problems Under Indeterminacy”, JISE, vol. 7, no. 1, pp. 106–121, 2023, doi: 10.38088/jise.1162151.
ISNAD Işık, Gürkan. “Fuzzy Modeling of Non-MCDM Problems Under Indeterminacy”. Journal of Innovative Science and Engineering 7/1 (June 2023), 106-121. https://doi.org/10.38088/jise.1162151.
JAMA Işık G. Fuzzy Modeling of non-MCDM Problems Under Indeterminacy. JISE. 2023;7:106–121.
MLA Işık, Gürkan. “Fuzzy Modeling of Non-MCDM Problems Under Indeterminacy”. Journal of Innovative Science and Engineering, vol. 7, no. 1, 2023, pp. 106-21, doi:10.38088/jise.1162151.
Vancouver Işık G. Fuzzy Modeling of non-MCDM Problems Under Indeterminacy. JISE. 2023;7(1):106-21.


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