Hybrid MCDM method on pythagorean fuzzy set and its application
Here in this article, a hybrid MCDM method on the Pythagorean fuzzy-environment is presented. This method is based on the Pythagorean Fuzzy Method based on Removal Effects of Criterion (PF-MEREC) and Stepwise Weight Assessment Ratio Analysis (SWARA) approaches. Here, the objective and subjective weights are assessed by PF-MEREC, SWARA model and the preference order ranking of the various alternatives is done through Complex Proportional Assessment (COPRAS) framework on the Pythagorean fuzzy set (PFS). The proposed method is the hybrid model of MEREC, SWARA and COPRAS methods. Further, the proposed model is used to identify the best banking management software (BMS) so that the bank can choose the robust bank management software tool to enhance its efficiency and excellence. Thereafter, a comparative discussion and sensitivity analysis of the proposed model is done with the existing techniques to judge the reasonability and efficiency of the proposed model.
Akram, M., Khan, A., & Borumand Saeid, A. (2021). Complex Pythagorean dombi fuzzy operators using aggregation operators and their decision‐making. Expert Systems, 38(2), 12626.
Ali, Z., Mahmood, T., Ullah, K., & Khan, Q. (2021). Einstein geometric aggregation operators using a novel complex interval-valued Pythagorean fuzzy setting with application in green supplier chain management. Reports in Mechanical Engineering, 2(1), 105-134. DOI: https://doi.org/10.31181/rme2001020105t
Alipour, M., Hafezi, R., Rani, P., Hafezi, M., & Mardani, A. (2021). A new Pythagorean fuzzy-based decision-making method through entropy measure for fuel cell and hydrogen components supplier selection. Energy, 234, 121208.
Ashraf, A., Ullah, K., Hussain, A., & Bari, M. (2022). Interval-valued picture fuzzy Maclaurin symmetric mean operator with application in multiple attribute decision-making. Reports in Mechanical Engineering, 3(1), 301-317. DOI: https://doi.org/10.31181/rme20020042022a
Atanassov, K. T. (1986). Intuitionistic fuzzy sets. Fuzzy Set System, 20(1), 87-96. DOI: https://doi.org/10.1016/S0165-0114(86)80034-3
Badi, I., & Pamucar, D. (2020). Supplier selection for steelmaking company by using combined Grey-MARCOS methods. Decision Making: Applications in Management and Engineering, 3(2), 37-48. DOI: https://doi.org/10.31181/dmame2003037b
Biswas, A., & Sarkar, B. (2019). Pythagorean fuzzy TOPSIS for multicriteria group decision-making with unknown weight information through entropy measure. International Journal Intelligence System, 34(6), 1108-1128.
Boran, F. E., Genc, S., Kurt, M., & Akay, D. (2009). A multi-criteria intuitionistic fuzzy group decision making for supplier selection with TOPSIS method. Expert System of Applications, 36(8), 11363–11368. DOI: https://doi.org/10.1016/j.eswa.2009.03.039
Bozanic, D., Jurisic, D., & Erkic, D. (2020). LBWA–Z-MAIRCA model supporting decision making in the army. Operational Research in Engineering Sciences: Theory and Applications, 3(2), 87-110. DOI: https://doi.org/10.31181/oresta2003087b
Chaurasiya, R., & Jain, D. (2021). Generalized intuitionistic fuzzy entropy on IF-MARCOS technique in multi-criteria decision making. In International Conference on Advances in Computing and Data Sciences, pp. 592-603. Springer, Cham.
Chaurasiya, R., & Jain, D. (2022). Pythagorean fuzzy entropy measure-based complex proportional assessment technique for solving multi-criteria healthcare waste treatment problem. Granular Computing, 1-14.
Chen, T. Y. (2019). Multiple criteria decision analysis under complex uncertainty: a Pearson-like correlation-based Pythagorean fuzzy compromise approach. International Journal Intelligence of Systems, 34(1), 114-151.
Durmic, E., Stevic, Z., Chatterjee, P., Vasiljevic, M., & Tomasevic, M. (2020). Sustainable supplier selection using combined FUCOM–Rough SAW model. Reports in mechanical engineering, 1(1), 34-43. DOI: https://doi.org/10.31181/rme200101034c
Ejegwa, P. A. (2020). Improved composite relation for Pythagorean fuzzy sets and its application to medical diagnosis. Granular Computing, 5(2), 277-286.
Farid, H. M. A., & Riaz, M. (2022). Pythagorean fuzzy prioritized aggregation operators with priority degrees for multi-criteria decision-making. International Journal of Intelligent Computing and Cybernetics, https://doi.org/10.1108/IJICC-10-2021-0224.
Garg, H. (2019). New logarithmic operational laws and their aggregation operators for Pythagorean fuzzy set and their applications. International Journal of Intelligent Systems, 34(1), 82-106.
Hadi, A., & Abdullah, M. Z. (2022). Web and IoT-based hospital location determination with criteria weight analysis. Bulletin of Electrical Engineering and Informatics, 11(1), 386-395.
Hezam, I. M., Mishra, A. R., Rani, P., Cavallaro, F., Saha, A., Ali, J., & Streimikiene, D. (2022). A hybrid intuitionistic fuzzy-MEREC-RS-DNMA method for assessing the alternative fuel vehicles with sustainability perspectives. Sustainability, 14(9), 5463.
Kaya, S. K. (2020). Evaluation of the effect of COVID-19 on Countries’ sustainable development level: A comparative MCDM framework. Operational Research in Engineering Sciences: Theory and Applications, 3(3), 101-122. DOI: https://doi.org/10.31181/oresta20303101k
Kersuliene, V., Zavadskas, E. K., & Turskis, Z. (2010). Selection of rational dispute resolution method by applying new step‐wise weight assessment ratio analysis (SWARA). Journal of business economics and management, 11(2), 243-258. DOI: https://doi.org/10.3846/jbem.2010.12
Keshavarz-Ghorabaee, M. (2021). Assessment of distribution center locations using a multi-expert subjective–objective decision-making approach. Scientific Reports, 11(1), 1-19.
Keshavarz-Ghorabaee, M., Amiri, M., Zavadskas, E. K., Turskis, Z., & Antucheviciene, J. (2021). Determination of objective weights using a new method based on the removal effects of criteria (MEREC). Symmetry, 13(4), 525.
Kreca, M., & Barac, D. (2015). Comparative analysis of core banking solutions in Serbia. Management, 20(76), 11-22. DOI: https://doi.org/10.7595/management.fon.2015.0019
Kumari, R., & Mishra, A. R. (2020). Multi-criteria COPRAS method based on parametric measures for intuitionistic fuzzy sets: application of green supplier selection. Iranian Journal of Science and Technology -Transactions of Electrical Engineering, doi:10.1007/s40998-020-00312-w.
Laudon, K. C., & Laudon, J. P. (2015). Management information system. Pearson education India, https://doi.org/10.1590/S1415- 65552003000100014.
Li, H., Cao, Y., & Su, L. (2022). Pythagorean fuzzy multi-criteria decision-making approach based on Spearman rank correlation coefficient. Soft Computing, 26(6), 3001-3012.
Marinkovic, M., Zavadskas, E. K., Matic, B., Jovanovic, S., Das, D. K., & Sremac, S. (2022). Application of wasted and recycled materials for production of stabilized layers of road structures. Buildings, 12(5), 552.
Mishra, A. R., Pamucar, D., Hezam, I. M., Chakrabortty, R. K., Rani, P., Bozanic, D., & Cirovic, G. (2022). Interval-valued Pythagorean fuzzy similarity measure-based complex proportional assessment method for waste-to-energy technology selection. Processes, 10(5), 1015.
Mishra, A. R., Rani, P., & Pardasani, K. R. (2019). Multiple-criteria decision-making for service quality selection based on Shapley COPRAS method under hesitant fuzzy sets. Granular Computing, 4(3), 435-449. DOI: https://doi.org/10.1007/s41066-018-0103-8
Mishra, A. R., Rani, P., Krishankumar, R., Zavadskas, E. K., Cavallaro, F., & Ravichandran, K.S. (2021). A hesitant fuzzy combined compromise solution framework-based on discrimination measure for ranking sustainable third-party reverse logistic providers. Sustainability, 13(4), 2064.
Mishra, A. R., Rani, P., Mardani, A., Pardasani, K. R., Govindan, K., & Alrasheedi, M. (2020). Healthcare evaluation in hazardous waste recycling using novel interval-valued intuitionistic fuzzy information based on complex proportional assessment method. Computers & Industrial Engineering, 139, 106140.
Mishra, A. R., Saha, A., Rani, P., Hezam, I. M., Shrivastava, R., & Smarandache, F. (2022). An integrated decision support framework using single-valued-MEREC-MULTIMOORA for low carbon tourism strategy assessment. IEEE Access, 10, 24411-24432.
Nguyen, H. Q., Nguyen, V. T., Phan, D. P., Tran, Q. H., & Vu, N. P. (2022). Multi-criteria decision making in the PMEDM process by using MARCOS, TOPSIS, and MAIRCA methods. Applied Sciences, 12(8), 3720.
Ozdemir, Y., & Gul, M. (2019). Measuring development levels of NUTS-2 regions in Turkey based on capabilities approach and multi-criteria decision-making. Computer and Industrial Engineering, 128, 150-169.
Pamucar, D. (2020). Normalized weighted geometric Dombi Bonferroni mean operator with interval grey numbers: Application in multicriteria decision making. Reports in Mechanical Engineering, 1(1), 44-52. DOI: https://doi.org/10.31181/rme200101044p
Pamucar, D., & Jankovic, A. (2020). The application of the hybrid interval rough weighted Power-Heronian operator in multi-criteria decision making. Operational Research in Engineering Sciences: Theory and Applications, 3(2), 54-73. DOI: https://doi.org/10.31181/oresta2003049p
Peng, X., & Li, W. (2019). Algorithms for interval-valued pythagorean fuzzy sets in emergency decision making based on multiparametric similarity measures and WDBA. IEEE Access, 7, 7419-7441.
Peng, X., Yuan, H., & Yang, Y. (2017). Pythagorean fuzzy information measures and their applications. International Journal of Intelligent Systems, 32(10), 991-1029. DOI: https://doi.org/10.1002/int.21880
Peng, X., Zhang, X., & Luo, Z. (2020). Pythagorean fuzzy MCDM method based on CoCoSo and CRITIC with score function for 5G industry evaluation. Artificial Intelligence Review, 53(5), 3813-3847.
Petrovic, G., Mihajlovic, J., Cojbasic, Z., Madic, M., & Marinkovic, D. (2019). Comparison of three fuzzy MCDM methods for solving the supplier selection problem. Facta Universitatis. Series: Mechanical Engineering,17(3), 455-469.
Puska, A., Stojanovic, I., Maksimovic, A., & Osmanovic, N. (2020). Evaluation software of project management used measurement of alternatives and ranking according to compromise solution (MARCOS) method. Operational Research in Engineering Sciences: Theory and Applications, 3(1), 89-102. DOI: https://doi.org/10.31181/oresta2001089p
Rani, P., & Jain, D. (2019). Information measures-based multi-criteria decision-making problems for interval-valued intuitionistic fuzzy environment. Proceeding of the National Academy of Science India section A Physical Sciences, 90(3), 535-546.
Rani, P., Mishra, A. R., & Mardani, A. (2020a). An extended Pythagorean fuzzy complex proportional assessment approach with new entropy and score function: Application in pharmacological therapy selection for type 2 diabetes. Applied Soft Computing, 94, 106441.
Rani, P., Mishra, A. R., Krishankumar, R., Ravichandran, K. S., Gandomi, A. H. (2020). A new Pythagorean fuzzy-based decision framework for assessing healthcare waste treatment. IEEE Transactions on Engineering Management, https://doi.org./10.1109 /TEM.2020. 3023 707.
Rani, P., Mishra, A. R., Saha, A., Hezam, I. M., & Pamucar, D. (2022). Fermatean fuzzy Heronian mean operators and MEREC‐based additive ratio assessment method: An application to food waste treatment technology selection. International Journal of Intelligent Systems, 37(3), 2612-2647.
Rong, Y., Pei, Z., & Liu, Y. (2020). Linguistic Pythagorean einstein operators and their application to decision making. Information, 11(1), 1-23.
Saraji, M. K., Mardani, A., Koppen, M., Mishra, A. R., & Rani, P. (2022). An extended hesitant fuzzy set using SWARA-MULTIMOORA approach to adapt online education for the control of the pandemic spread of COVID-19 in higher education institutions. Artificial Intelligence Review, 55(1), 181-206.
Song, H., & Chen, Z. C. (2021). Multi-attribute decision-making method-based distance and COPRAS method with probabilistic hesitant fuzzy environment. International Journal of Computational Intelligence Systems, 14(1), 1229-1241.
Tesic, D., Bozanic, D., Pamucar, D., & Din, J. (2022). DIBR - Fuzzy MARCOS model for selecting a location for a heavy mechanized bridge. Military Technical Courier, 70(2), 314-339.
Wang, J., Gao, H., & Wei, G. (2019). The generalized dice similarity measures for Pythagorean fuzzy multiple attribute group decision making. International Journal of Intelligent Systems, 34(6), 1158–1183.
Wei, G.W. (2019). Pythagorean fuzzy Hamacher power aggregation operators in multiple attribute decision making. Fund Inform, 166(1), 57–85.
Xu, T. T., Zhang, H., & Li, B. Q. (2020). Pythagorean fuzzy entropy and its application in multiple-criteria decision-making. International Journal of Fuzzy Systems, 22(5), 1552-1564.
Yager, R. R. (2013). Pythagorean fuzzy subsets. In Proceedings of the 2013 Joint IFSA World Congress and NAFIPS Annual Meeting, Edmonton, AB, Canada 57-61. DOI: https://doi.org/10.1109/IFSA-NAFIPS.2013.6608375
Yager, R. R. (2013a). Pythagorean membership grades in multicriteria decision making. IEEE Transactions Fuzzy System, 22(4), 958-965. DOI: https://doi.org/10.1109/TFUZZ.2013.2278989
Yager, R. R., & Abbasov, A. M. (2013b). Pythagorean membership grades, complex numbers, and decision making. International Journal of Intelligent Systems, 28(5), 436-452. DOI: https://doi.org/10.1002/int.21584
Yildirim, B. F., & Yildirim, S. K. (2022). Evaluating the satisfaction level of citizens in municipality services by using picture fuzzy VIKOR method: 2014-2019 period analysis. Decision Making: Applications in Management and Engineering, 5(1), 50-66. DOI: https://doi.org/10.31181/dmame181221001y
Zadeh, L. A. (1965). Fuzzy sets. Information Control, 8(3), 338-353. DOI: https://doi.org/10.1016/S0019-9958(65)90241-X
Zavadskas, E. K., Kaklauskas, A., & Sarka, V. (1994). The new method of multicriteria complex proportional assessment of projects. Technological and Economical Development of Economy, 1(3), 131-139.
Zavadskas, E. K., Turskis, Z., Stevic, Z., & Mardani, A. (2020). Modelling procedure for the selection of steel pipes supplier by applying fuzzy AHP method. Operational Research in Engineering Sciences: Theory and Applications, 3(2), 39-53. DOI: https://doi.org/10.31181/oresta2003034z
Zhang, X., & Xu, Z. (2014). Extension of TOPSIS to multiple criteria decision making with Pythagorean fuzzy sets. International Journal of Intelligent Systems, 29(12), 1061-1078. DOI: https://doi.org/10.1002/int.21676
Zheng, Y., Xu, Z., He, Y., & Liao, H. (2018). Severity assessment of chronic obstructive pulmonary disease based on hesitant fuzzy linguistic COPRAS method. Applied Soft Computing, 69, 60-71. DOI: https://doi.org/10.1016/j.asoc.2018.04.035
Zizovic, M., & Pamucar, D. (2019). New model for determining criteria weights: Level Based Weight Assessment (LBWA) model. Decision Making: Applications in Management and Engineering, 2(2), 126-137. DOI: https://doi.org/10.31181/dmame1902102z