How to cite this paper
APA: Bairagi, B & Sarkar, B. (2026). Enhanced decision-making in uncertain environments: A Fermatean fuzzy approach for heterogeneous group dynamics. Uncertain Supply Chain Management, 14(2), 113-146.
Chicago/Turabian: Bairagi, B & Sarkar, B. 2026. "Enhanced decision-making in uncertain environments: A Fermatean fuzzy approach for heterogeneous group dynamics." Uncertain Supply Chain Management 14, no. 2 (2026): 113-146.
AMA: Bairagi, B & Sarkar, B. Enhanced decision-making in uncertain environments: A Fermatean fuzzy approach for heterogeneous group dynamics. Uncertain Supply Chain Management. 2026;14(2):113-146.
References
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Akram, M., Shahzadi, G., & Ahmadini, A. A. H. (2020). Decision-making method for an effective sanitizer to reduce COVID-19 under Fermatean fuzzy sets. Journal of Mathematics, 1-19. https://doi.org/10.1155/2020/3263407
Atanassov, K. T. (1986). Intuitionistic fuzzy sets. Fuzzy Sets and Systems, 20(1), 87–96.
Aydoğan, H., & Ozkir, V. (2024). A Fermatean fuzzy MCDM method for selection and ranking problems: Case studies. Expert Systems with Applications, 237, 121628. https://doi.org/10.1016/j.eswa.2023.121628
Bairagi, B., Dey, B., Sarkar, B., & Sanyal, S. K. (2015). A De Novo multi-approaches multi-criteria decision making technique with an application in performance evaluation of material handling devices. Computers and Industrial Engineering, 87, 267–282.
Biswas, A., & Deb, N. (2021). Pythagorean fuzzy Schweizer and Sklar power aggregation operators for solving multi-attribute decision-making problems. Granular Computing, 6(4), 991–1007.
Bose, U., Davey, A. M., & Olson, D. L. (1997). Multi-attribute utility methods in group decision making: Past applications and potential for inclusion in GDSS. Omega, 25, 691–706.
Brown, A., & Green, B. (2022). Advancements in Fermatean fuzzy logic for decision-making under uncertainty. Journal of Uncertain Systems, 15(2), 123-136.
Chen, X., Zhang, H., & Dong, Y. (2015). The fusion process with heterogeneous preference structures in group decision making. Information Fusion, 24, 72–83.
Chou, Y. C., Yao, H. C., & Chun, Y. S. (2008). A fuzzy simple additive weighting system under group decision-making for facility location selection with objective/subjective attributes. European Journal of Operational Research, 189, 132–145.
Dey, B., Bairagi, B., Sarkar, B., & Sanyal, S. K. (2017). Group heterogeneity in multi-member decision-making model with an application to warehouse location selection in a supply chain. Computers & Industrial Engineering, 105, 101–122.
Dey, B., Bairagi, B., Sarkar, B., & Sanyal, S. K. (2013). A hybrid fuzzy technique for the selection of warehouse location in a supply chain under a utopian environment. International Journal of Management Science and Engineering Management. 8(4), 250-261. https://doi.org/10.1080/17509653.2013.825075
Fan, Z. P., Xiao, S. H., & Hu, G. F. (2004). An optimization method for integrating two kinds of preference information in group decision-making. Computers and Industrial Engineering, 46(20), 329–335.
Garcia, D., & Martinez, M. (2019). Managing heterogeneous group dynamics in organizational decision-making. Journal of Organizational Behavior, 42(3), 321–335.
Garg, H., Shahzadi, G., & Akram, M. (2020). Decision-making analysis based on Fermatean fuzzy Yager operators with application in COVID-19 testing facility. Mathematical Problems in Engineering, 1-16. https://doi.org/10.1155/2020/7279027
Hadi, A., Khan, W., & Khan, A. (2021). A novel approach to MADM problems using Fermatean fuzzy Hamacher aggregation operators. International Journal of Intelligent Systems, 36(7), 3464–3499.
Hedi-Venchech, A., & Mirjaberi, M. (2014). Fuzzy inferior ratio method for multiple attribute decision-making problem. Information Sciences, 277, 263–272.
Ibrahim, H. A., Qahtan, S., Zaidan, A. A., Deveci, M., Hajiaghaei-Keshteli, M., Mohammed, R. T., & Alamoodi, A. H. (2024). Sustainability in mobility for autonomous vehicles over smart city evaluation: Using interval-valued Fermatean fuzzy rough set-based decision-making model. Engineering Applications of Artificial Intelligence, 129, 107609.
Jones, E., & Smith, K. (2019). Challenges and limitations of traditional decision-making methods in uncertain environments. Journal of Decision Making, 18(3), 211–225.
Kose, Y., Ayyildiz, E., & Cevikcan, E. (2024). Evaluation of disassembly line layouts using an integrated Fermatean fuzzy decision-making methodology: An application for refrigerator disassembly line. Computers & Industrial Engineering, 190, 110090.
Kose, Y., Cevikcan, E., Ertemel, S., & Murat, M. (2023). Game theory-oriented approach for disassembly line worker assignment and balancing problem with multi-manned workstations. Computers and Industrial Engineering, 181. https://doi.org/10.1016/J.CIE.2023.109294
Kose, Y., Civan, H. N., Ayyildiz, E., & Cevikcan, E. (2022). An interval-valued Pythagorean fuzzy AHP-TOPSIS integrated model for ergonomic assessment of setup process under SMED. Sustainability, 14(21), 13804. https://doi.org/10.3390/SU142113804
Kirişci, M., Demir, I., & Şimşek, N. (2022). Fermatean fuzzy ELECTRE multi-criteria group decision-making and most suitable biomedical material selection. Artificial Intelligence in Medicine, 127. https://doi.org/10.1016/j.artmed.2022.102278
Mishra, A. R., & Rani, P. (2021). Multi-criteria healthcare waste disposal location selection based on Fermatean fuzzy WASPAS method. Complex & Intelligent Systems, 7(5), 2469–2484. https://doi.org/10.1007/s40747-021-00407-9
Perez, I. J., Alonso, S., Cabrerizo, F. J., Lu, J., & Herrera-Viedma, E. (2011). Modeling. Springer-Verlag Berlin Heidelberg, LNAI, 6820, 55–61.
Seikh, M., & Mandal, R. U. (2021). Intuitionistic fuzzy Dombi aggregation operators and their application to multiple attribute decision-making. Granular Computing, 6(3), 473–488.
Sen, S., Patra, K., & Mondal, S. K. (2021). A new approach to similarity measure for generalized trapezoidal fuzzy numbers and its application to fuzzy risk analysis. Granular Computing, 6(3), 705–718.
Senapati, T., & Yager, R. R. (2019a). Fermatean fuzzy weighted operators and its application in multi-attribute decision-making models. Engineering Applications in Artificial Intelligence, 85, 112–121.
Senapati, T., & Yager, R. R. (2019b). Some new operations over Fermatean fuzzy numbers and application of Fermatean fuzzy WPM in multiple criteria decision making. Informatica, 30(2), 391–412. https://doi.org/10.15388/Informatica.2019.211
Senapati, T., & Yager, R. R. (2020). Fermatean fuzzy sets. Journal of Ambient Intelligence and Humanized Computing, 11(2), 663–674.
Smith, J. (2018). Introduction to decision-making in uncertain environments. International Journal of Decision Making, 25(1), 45–58.
Torra, V. (2010). Hesitant fuzzy sets. International Journal of Intelligent Systems, 25(6), 529–539.
Verma, R., & Mittal, A. (2022). Multiple attribute group decision-making based on novel probabilistic ordered weighted cosine similarity operators with Pythagorean fuzzy information. Granular Computing, 8, 1–16. https://doi.org/10.1007/s41066-022-00318-1
Yager, R. R. (2014). Pythagorean membership values in multi-attribute decision making. IEEE Transactions on Fuzzy Systems, 22(4), 958–965.
Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353.
Zhang, X., Xu, Z., & Wang, H. (2015). Heterogeneous multiple criteria group decision making with incomplete weight information: A deviation modeling approach. Information Fusion, 25, 49–62.
Akram, M., Shahzadi, G., & Ahmadini, A. A. H. (2020). Decision-making method for an effective sanitizer to reduce COVID-19 under Fermatean fuzzy sets. Journal of Mathematics, 1-19. https://doi.org/10.1155/2020/3263407
Atanassov, K. T. (1986). Intuitionistic fuzzy sets. Fuzzy Sets and Systems, 20(1), 87–96.
Aydoğan, H., & Ozkir, V. (2024). A Fermatean fuzzy MCDM method for selection and ranking problems: Case studies. Expert Systems with Applications, 237, 121628. https://doi.org/10.1016/j.eswa.2023.121628
Bairagi, B., Dey, B., Sarkar, B., & Sanyal, S. K. (2015). A De Novo multi-approaches multi-criteria decision making technique with an application in performance evaluation of material handling devices. Computers and Industrial Engineering, 87, 267–282.
Biswas, A., & Deb, N. (2021). Pythagorean fuzzy Schweizer and Sklar power aggregation operators for solving multi-attribute decision-making problems. Granular Computing, 6(4), 991–1007.
Bose, U., Davey, A. M., & Olson, D. L. (1997). Multi-attribute utility methods in group decision making: Past applications and potential for inclusion in GDSS. Omega, 25, 691–706.
Brown, A., & Green, B. (2022). Advancements in Fermatean fuzzy logic for decision-making under uncertainty. Journal of Uncertain Systems, 15(2), 123-136.
Chen, X., Zhang, H., & Dong, Y. (2015). The fusion process with heterogeneous preference structures in group decision making. Information Fusion, 24, 72–83.
Chou, Y. C., Yao, H. C., & Chun, Y. S. (2008). A fuzzy simple additive weighting system under group decision-making for facility location selection with objective/subjective attributes. European Journal of Operational Research, 189, 132–145.
Dey, B., Bairagi, B., Sarkar, B., & Sanyal, S. K. (2017). Group heterogeneity in multi-member decision-making model with an application to warehouse location selection in a supply chain. Computers & Industrial Engineering, 105, 101–122.
Dey, B., Bairagi, B., Sarkar, B., & Sanyal, S. K. (2013). A hybrid fuzzy technique for the selection of warehouse location in a supply chain under a utopian environment. International Journal of Management Science and Engineering Management. 8(4), 250-261. https://doi.org/10.1080/17509653.2013.825075
Fan, Z. P., Xiao, S. H., & Hu, G. F. (2004). An optimization method for integrating two kinds of preference information in group decision-making. Computers and Industrial Engineering, 46(20), 329–335.
Garcia, D., & Martinez, M. (2019). Managing heterogeneous group dynamics in organizational decision-making. Journal of Organizational Behavior, 42(3), 321–335.
Garg, H., Shahzadi, G., & Akram, M. (2020). Decision-making analysis based on Fermatean fuzzy Yager operators with application in COVID-19 testing facility. Mathematical Problems in Engineering, 1-16. https://doi.org/10.1155/2020/7279027
Hadi, A., Khan, W., & Khan, A. (2021). A novel approach to MADM problems using Fermatean fuzzy Hamacher aggregation operators. International Journal of Intelligent Systems, 36(7), 3464–3499.
Hedi-Venchech, A., & Mirjaberi, M. (2014). Fuzzy inferior ratio method for multiple attribute decision-making problem. Information Sciences, 277, 263–272.
Ibrahim, H. A., Qahtan, S., Zaidan, A. A., Deveci, M., Hajiaghaei-Keshteli, M., Mohammed, R. T., & Alamoodi, A. H. (2024). Sustainability in mobility for autonomous vehicles over smart city evaluation: Using interval-valued Fermatean fuzzy rough set-based decision-making model. Engineering Applications of Artificial Intelligence, 129, 107609.
Jones, E., & Smith, K. (2019). Challenges and limitations of traditional decision-making methods in uncertain environments. Journal of Decision Making, 18(3), 211–225.
Kose, Y., Ayyildiz, E., & Cevikcan, E. (2024). Evaluation of disassembly line layouts using an integrated Fermatean fuzzy decision-making methodology: An application for refrigerator disassembly line. Computers & Industrial Engineering, 190, 110090.
Kose, Y., Cevikcan, E., Ertemel, S., & Murat, M. (2023). Game theory-oriented approach for disassembly line worker assignment and balancing problem with multi-manned workstations. Computers and Industrial Engineering, 181. https://doi.org/10.1016/J.CIE.2023.109294
Kose, Y., Civan, H. N., Ayyildiz, E., & Cevikcan, E. (2022). An interval-valued Pythagorean fuzzy AHP-TOPSIS integrated model for ergonomic assessment of setup process under SMED. Sustainability, 14(21), 13804. https://doi.org/10.3390/SU142113804
Kirişci, M., Demir, I., & Şimşek, N. (2022). Fermatean fuzzy ELECTRE multi-criteria group decision-making and most suitable biomedical material selection. Artificial Intelligence in Medicine, 127. https://doi.org/10.1016/j.artmed.2022.102278
Mishra, A. R., & Rani, P. (2021). Multi-criteria healthcare waste disposal location selection based on Fermatean fuzzy WASPAS method. Complex & Intelligent Systems, 7(5), 2469–2484. https://doi.org/10.1007/s40747-021-00407-9
Perez, I. J., Alonso, S., Cabrerizo, F. J., Lu, J., & Herrera-Viedma, E. (2011). Modeling. Springer-Verlag Berlin Heidelberg, LNAI, 6820, 55–61.
Seikh, M., & Mandal, R. U. (2021). Intuitionistic fuzzy Dombi aggregation operators and their application to multiple attribute decision-making. Granular Computing, 6(3), 473–488.
Sen, S., Patra, K., & Mondal, S. K. (2021). A new approach to similarity measure for generalized trapezoidal fuzzy numbers and its application to fuzzy risk analysis. Granular Computing, 6(3), 705–718.
Senapati, T., & Yager, R. R. (2019a). Fermatean fuzzy weighted operators and its application in multi-attribute decision-making models. Engineering Applications in Artificial Intelligence, 85, 112–121.
Senapati, T., & Yager, R. R. (2019b). Some new operations over Fermatean fuzzy numbers and application of Fermatean fuzzy WPM in multiple criteria decision making. Informatica, 30(2), 391–412. https://doi.org/10.15388/Informatica.2019.211
Senapati, T., & Yager, R. R. (2020). Fermatean fuzzy sets. Journal of Ambient Intelligence and Humanized Computing, 11(2), 663–674.
Smith, J. (2018). Introduction to decision-making in uncertain environments. International Journal of Decision Making, 25(1), 45–58.
Torra, V. (2010). Hesitant fuzzy sets. International Journal of Intelligent Systems, 25(6), 529–539.
Verma, R., & Mittal, A. (2022). Multiple attribute group decision-making based on novel probabilistic ordered weighted cosine similarity operators with Pythagorean fuzzy information. Granular Computing, 8, 1–16. https://doi.org/10.1007/s41066-022-00318-1
Yager, R. R. (2014). Pythagorean membership values in multi-attribute decision making. IEEE Transactions on Fuzzy Systems, 22(4), 958–965.
Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353.
Zhang, X., Xu, Z., & Wang, H. (2015). Heterogeneous multiple criteria group decision making with incomplete weight information: A deviation modeling approach. Information Fusion, 25, 49–62.