A Circular Pythagorean Fuzzy Credibility Graph (CPyF-CG) Based TOPSIS Approach for MADM and its Application in Renewable Energy
DOI:
https://doi.org/10.67334/cds31202644Keywords:
Multi-attribute decision making (MADM), Fuzzy TOPSIS, Circular Pythagorean fuzzy sets, Credibility graph, Uncertainty modeling, Interdependence of alternatives, Weighted aggregation, Renewable energy planningAbstract
Multi-attribute decision-making (MADM) often requires combining judgments from multiple experts under uncertainty, hesitancy, and varying levels of credibility. Although classical TOPSIS and its fuzzy extensions provide useful rankings of alternatives, they generally treat alternatives as independent and overlook credibility-based interdependencies among them. To address this limitation, this study develops a TOPSIS framework based on the Circular Pythagorean Fuzzy Credibility Graph (CPyF-CG). Within the proposed framework, experts provide evaluations expressed as circular Pythagorean fuzzy credibility values, incorporating membership, non-membership, a radius representing uncertainty, and a credibility parameter. These assessments are aggregated using the CPyF weighted averaging operator while preserving admissibility within the fuzzy domain. A three-stage procedure comprising raw scoring, normalization, and credibility scaling transforms the aggregated values into a crisp decision matrix. The same procedure is applied to attribute weights. The main contribution of the CPyF-CG framework lies in its graph structure, in which credibility-weighted edges represent interdependencies among alternatives. The final closeness coefficients are adjusted using edge-based credibility factors, thereby incorporating both individual alternative performance and the reliability of their connections. The applicability of the proposed framework is illustrated through a case study of renewable energy planning in West Bengal, India. The results identify solar energy and small hydropower as the most suitable alternatives under the proposed evaluation framework.
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Chen, C.-T. (2000). Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Sets and Systems, 114(1), 1–9. https://doi.org/10.1016/S0165-0114(97)00377-1 DOI: https://doi.org/10.1016/S0165-0114(97)00377-1
Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353. https://doi.org/10.1016/S0019-9958(65)90241-X DOI: https://doi.org/10.1016/S0019-9958(65)90241-X
Muralidharan, C., Anantharaman, N., & Deshmukh, S. G. (2002). A multi-criteria group decision-making model for supplier rating. Journal of Supply Chain Management, 38(3), 22–33. https://doi.org/10.1111/j.1745-493X.2002.tb00140.x DOI: https://doi.org/10.1111/j.1745-493X.2002.tb00140.x
Kumar, M., Vrat, P., & Shankar, R. (2004). A fuzzy goal programming approach for vendor selection problem in a supply chain. Computers & Industrial Engineering, 46(1), 69–85. https://doi.org/10.1016/j.cie.2003.09.010 DOI: https://doi.org/10.1016/j.cie.2003.09.010
Shyur, H.-J., & Shih, H.-S. (2006). A hybrid MCDM model for strategic vendor selection. Mathematical and Computer Modelling, 44(7–8), 749–761. https://doi.org/10.1016/j.mcm.2005.04.018 DOI: https://doi.org/10.1016/j.mcm.2005.04.018
Li, G.-D., Yamaguchi, D., & Nagai, M. (2007). A grey-based decision-making approach to the supplier selection problem. Mathematical and Computer Modelling, 46(3–4), 573–581. https://doi.org/10.1016/j.mcm.2006.11.021 DOI: https://doi.org/10.1016/j.mcm.2006.11.021
Jadidi, O., Yusuff, R. M., Firouzi, F., & Hong, T. S. (2008). Improvement of a grey based method for supplier selection problem. Journal of Achievements in Materials and Manufacturing Engineering, 31(2), 770–777.
Sreekumar, S., & Mahapatra, S. S. (2009). A fuzzy multi-criteria decision making approach for supplier selection in supply chain management. African Journal of Business Management, 3(4), 168–177.
Gheidar-Kheljani, J., Ghodsypour, S. H., & Fatemi Ghomi, S. M. T. (2010). Supply chain optimization policy for a supplier selection problem: A mathematical programming approach. Iranian Journal of Operations Research, 2(1), 17–31.
Vaezi, Z., Shahgholian, K., & Shahraki, A. (2011). A model for supplier selection based on fuzzy multi-criteria group decision making. Advances in Management & Applied Economics, 1(2), 207–219.
Yager, R. R., & Abbasov, A. M. (2013). Pythagorean membership grades, complex numbers, and decision making. International Journal of Intelligent Systems, 28(5), 436–452. https://doi.org/10.1002/int.21584 DOI: https://doi.org/10.1002/int.21584
Peng, X., & Yang, Y. (2015). Some results for Pythagorean fuzzy sets. International Journal of Intelligent Systems, 30(11), 1133–1160. https://doi.org/10.1002/int.21738 DOI: https://doi.org/10.1002/int.21738
Li, H., Su, L., Cao, Y., & Lv, L. (2019). A Pythagorean fuzzy TOPSIS method based on similarity measure and its application to project delivery system selection. Journal of Intelligent & Fuzzy Systems, 37(5), 7059–7071. https://doi.org/10.3233/JIFS-181690 DOI: https://doi.org/10.3233/JIFS-181690
Senapati, T., & Yager, R. R. (2020). Fermatean fuzzy sets. Journal of Ambient Intelligence and Humanized Computing, 11(2), 663–674. https://doi.org/10.1007/s12652-019-01377-0 DOI: https://doi.org/10.1007/s12652-019-01377-0
Gul, M., Lo, H.-W., & Yucesan, M. (2021). Fermatean fuzzy TOPSIS-based approach for occupational risk assessment in manufacturing. Complex & Intelligent Systems, 7(5), 2635–2653. https://doi.org/10.1007/s40747-021-00417-7 DOI: https://doi.org/10.1007/s40747-021-00417-7
Hajiaghaei-Keshteli, M., Cenk, Z., Erdebilli, B., Özdemir, Y. S., & Gholian-Jouybari, F. (2023). Pythagorean fuzzy TOPSIS method for green supplier selection in the food industry. Expert Systems with Applications, 224, Article 120036. https://doi.org/10.1016/j.eswa.2023.120036 DOI: https://doi.org/10.1016/j.eswa.2023.120036
Bozyiğit, M. C., Olgun, M., & Ünver, M. (2023). Circular Pythagorean fuzzy sets and applications to multi-criteria decision making. Informatica, 34(4), 713–742. https://doi.org/10.15388/23-INFOR529 DOI: https://doi.org/10.15388/23-INFOR529
Tüysüz, N., & Kahraman, C. (2023). An integrated picture fuzzy Z-AHP & TOPSIS methodology: Application to solar panel selection. Applied Soft Computing, 149, Article 110951. https://doi.org/10.1016/j.asoc.2023.110951 DOI: https://doi.org/10.1016/j.asoc.2023.110951
Sulaiman, R., Astuti, Y. P., Yunianti, D. N., & Awang, N. A. (2024). On the picture fuzzy-TOPSIS method. European Journal of Pure and Applied Mathematics, 17(3), 1727–1736. https://doi.org/10.29020/nybg.ejpam.v17i3.5225 DOI: https://doi.org/10.29020/nybg.ejpam.v17i3.5225
Mukherjee, S., & Kar, S. (2013). A three phase supplier selection method based on fuzzy preference degree. Journal of King Saud University – Computer and Information Sciences, 25(2), 173–185. https://doi.org/10.1016/j.jksuci.2012.11.001 DOI: https://doi.org/10.1016/j.jksuci.2012.11.001
Li, J., & Zhang, Y. (2025). A modified TOPSIS algorithm for the assessment of sports quality in higher education using circular Pythagorean fuzzy information. Scientific Reports, 15, Article 18846. https://doi.org/10.1038/s41598-025-02211-8 DOI: https://doi.org/10.1038/s41598-025-02211-8
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