An intuitionstic fuzzy factorial analysis model for multi-attribute decision-making under random environment

References

• Ahn, B. S. (2015). Extreme point-based multi-attribute decision analysis with incomplete information. European Journal of Operational Research, 240(3), 748–755.10.1016/j.ejor.2014.07.037
   Web of Science ®Google Scholar
• Atanassov, K. T. (1986). Intuitionistic fuzzy sets. Fuzzy Sets and Systems, 20(1), 87–96.10.1016/S0165-0114(86)80034-3
   Web of Science ®Google Scholar
• Atanassov, K. T. (1989). More on intuitionistic fuzzy sets. Fuzzy Sets and Systems, 51(1), 117–118.
   Google Scholar
• Atanassov, K. T. (2012). On intuitionistic fuzzy sets theory. Berlin: Springer-Verlag.10.1007/978-3-642-29127-2
   Google Scholar
• Beliakov, G., James, S., Mordelová, J., Rückschlossová, T., & Yager, R. R. (2010). Generalized Bonferroni mean operators in multi-criteria aggregation. Fuzzy Sets and Systems, 161(17), 2227–2242.10.1016/j.fss.2010.04.004
   Web of Science ®Google Scholar
• Belton, V., & Stewart, T. J. (2002). Multiple criteria decision analysis–an integrated approach. Boston, MA: Kluwer.10.1007/978-1-4615-1495-4
   Google Scholar
• Boran, F. E., Genç, S., Kurt, M., & Akay, D. (2009). A multi-criteria intuitionistic fuzzy group decision making for supplier selection with TOPSIS method. Expert Systems with Applications, 36(8), 11363–11368.10.1016/j.eswa.2009.03.039
   Web of Science ®Google Scholar
• Bratianu, C., & Vasilache, S. (2010). A factorial analysis of the managerial linear thinking model. International Journal of Innovation & Learning, 8(4), 4393–4407.
   Google Scholar
• Chakrabortty, S., Pal, M., & Nayak, P. K. (2013). Intuitionistic fuzzy optimization technique for pareto optimal solution of manufacturing inventory models with short-ages. European Journal of Operational Research, 228, 381–387.10.1016/j.ejor.2013.01.046
   Web of Science ®Google Scholar
• Charpentier, J. P., & Beaujean, J. M. (1976). Toward a better understanding energy consumption—II — factor analysis: A new approach to energy demand. Energy, 228(2), 413–428.10.1016/0360-5442(76)90070-0
   Google Scholar
• Chen, S. M., & Tan, J. M. (1994). Handling multicriteria fuzzy decision-making problems based on vague set theory. Fuzzy Sets and Systems, 67(2), 163–172.10.1016/0165-0114(94)90084-1
   Web of Science ®Google Scholar
• Choquet, G. (1953). Theory of capacities. Annales of De l’Institut Fourier, 5, 131–295.
   Google Scholar
• Cuss, C. W., McConnell, S. M., & Guéguen, C. (2016). Combining parallel factor analysis and machine learning for the classification of dissolved organic matter according to source using fluorescence signatures. Chemospher, 155, 283–291.10.1016/j.chemosphere.2016.04.061
   Google Scholar
• Fan, Z. P., Zhang, X., Chen, F. D., & Liu, Y. (2013). Extended TODIM method for hybrid MADM problems. Knowledge-Based Systems, 42(2), 40–48.10.1016/j.knosys.2012.12.014
   Google Scholar
• Guo, K. H., & Li, W. L. (2012). An attitudinal-based method for constructing intuitionistic fuzzy information in hybrid MADM under uncertainty. Information, Science, 208(15), 28–38.10.1016/j.ins.2012.04.030
   Google Scholar
• Hong, D. H., & Choi, C. H. (2000). Multicriteria fuzzy decision-making problems based on vague set thoery. Fuzzy Sets and Systems, 114(1), 103–113.10.1016/S0165-0114(98)00271-1
   Web of Science ®Google Scholar
• Hung, K. C., & Lin, K. (2013). Long-term business cycle forecasting through a potential intuitionistic: Fuzzy least-squares support vector regression approach. Information Sciences, 224(2), 37–48.10.1016/j.ins.2012.10.033
   Google Scholar
• Hwang, C. L., & Yoon, K. (1981). Multiple attribute decision making: Methods and applications. Berlin: Springer-Verlag.10.1007/978-3-642-48318-9
   Google Scholar
• İrem, O., Oztaysi, B., Onar, S. C., & Kahraman, C. (2017). Multi-expert performance evaluation of healthcare institutions using an integrated intuitionistic fuzzy AHP&DEA methodology. Knowledge-Based Systems, 133, 90–106. doi:10.1016/j.knosys.2017.06.028
   Google Scholar
• Kannana, G., Pokharel, S., & Kumar, P. S. (2009). A hybrid approach using ISM and fuzzy TOPSIS for the selection of reverse logistics provider. Resources, Conservation and Recycling, 54(1), 28–36.10.1016/j.resconrec.2009.06.004
   Web of Science ®Google Scholar
• Keeney, R. L., & Raiffa, H. (1976). Decision with multiple objectives. New York, NY: Wiley.
   Google Scholar
• Ladrón, R., Cortés, G., & Porras, S. T. (2014). Estimation of the underlying structure of systematic risk with the use of principal component analysis and factor analysis. Contaduríay Administración, 59(3), 197–234.
   Google Scholar
• Lewi, P. J. (1989). Spectral map analysis: Factorial analysis of contrasts, especially from log ratios. Chemometrics & Intelligent Laboratory Systems, 5(2), 105–116.10.1016/0169-7439(89)80007-3
   Google Scholar
• Li, D. (2011). The GOWA operator based approach to multiattribute decision making using intuitionistic fuzzy sets. Mathematical and Computer Modelling, 53(5–6), 1182–1196.10.1016/j.mcm.2010.11.088
   Web of Science ®Google Scholar
• Lin, K. P., Chang, H. F., Chen, T. L., Lu, Y. M., & Wang, C. H. (2016). Intuitionistic fuzzy C-regression by using least squares support vector regression. Expert Systems with Applications, 64, 296–304.10.1016/j.eswa.2016.07.040
   Google Scholar
• Liu, B. S., Shen, Y. H., Chen, X. H., Chen, Y., & Wang, X. Q. (2015). An interval-valued intuitionistic fuzzy principal component analysis model-based method for complex multi-attribute large-group decision-making. European Journal of Operational Research, 245(1), 209–225.10.1016/j.ejor.2015.02.025
   Web of Science ®Google Scholar
• Liu, B. S., Shen, Y. H., Chen, X. H., Sun, H., & Chen, Y. (2014). A complex multi-attribute large-group PLS decision making method in the interval-valued intuitionistic fuzzy environment. Applied Mathematical Modelling, 38(17–18), 4512–4527.10.1016/j.apm.2014.02.023
   Google Scholar
• Lourenzutti, R., & Krohling, R. A. (2013). A study of TODIM in a intuitionistic fuzzy and random environment. Expert Systems with Applications, 40(16), 6459–6468.10.1016/j.eswa.2013.05.070
   Web of Science ®Google Scholar
• Lourenzutti, R., & Krohling, R. A. (2016). A generalized TOPSIS method for group decision making with heterogeneous information in a dynamic environment. Information Science, 330, 1–18.10.1016/j.ins.2015.10.005
   Web of Science ®Google Scholar
• Ondemir, O., & Gupta, M. S. (2014). A multi-criteria decision making model for advanced repair-to-order and disassembly-to-order system. European Journal of Operational Research, 233(2), 408–419.10.1016/j.ejor.2013.09.003
   Web of Science ®Google Scholar
• Perez, I. J., Cabrerizo, F. J., & Herrera-Viedma, E. (2010). A mobile decision support system for dynamic group decision-making problems. IEEE Transactions on Systems Man & Cybernetics Part A Systems & Humans, 40(6), 1244–1256.10.1109/TSMCA.2010.2046732
   Web of Science ®Google Scholar
• Puri, J., & Yadav, S. P. (2015). Intuitionistic fuzzy data envelopment analysis: An application to the banking sector in India. Expert Systems with Applications, 42(11), 4982–4998.10.1016/j.eswa.2015.02.014
   Google Scholar
• Song, X. N., Zheng, Y. J., Wu, X. J., Yang, X. B., & Yang, J. Y. (2010). A complete fuzzy discriminant analysis approach for face recognition. Applied Soft Computing, 10(1), 208–214.10.1016/j.asoc.2009.07.002
   Google Scholar
• Spearman, C. (1904). General intelligence, objectively determined and measured. American Journal of Psychology, 15(2), 201–292.10.2307/1412107
   Google Scholar
• Su, Z. X., Xia, G. P., Chen, M. Y., & Wang, L. (2012). Induced generalized intuitionistic fuzzy OWA operator for multi-attribute group decision making. Expert Systems with Applications, 39(2), 1902–1910.10.1016/j.eswa.2011.08.057
   Web of Science ®Google Scholar
• Tan, C. Q. (2011). A multi-criteria interval-valued intuitionistic fuzzy group decision making with Choquet integral-based TOPSIS. Expert Systems with Applications, 38(4), 3023–3033.10.1016/j.eswa.2010.08.092
   Web of Science ®Google Scholar
• Tan, C. Q., & Chen, X. H. (2010). Intuitionistic fuzzy Choquet integral operator for multi-criteria decision making. Expert Systems with Applications, 37(1), 149–157.10.1016/j.eswa.2009.05.005
   Web of Science ®Google Scholar
• Tan, C. Q., & Chen, X. H. (2014). Dynamic similarity measures between intuitionistic fuzzy sets and its application. International Journal of Fuzzy Systems, 16(4), 511–519.
   Google Scholar
• Tan, C. Q., Jiang, Z. Z., & Chen, X. H. (2015). Atanassov’s intuitionistic fuzzy Quasi-Choquet geometric operators and their applications to multicriteria decision making. Fuzzy Optimization and Decision Making, 14(2), 139–172.10.1007/s10700-014-9196-y
   Google Scholar
• Tan, C. Q., Yi, W. T., & Chen, X. H. (2015). Generalized intuitionistic fuzzy geometric aggregation operators for multi-criteria decision making. Journal of the Operational Research Society, 66(11), 1919–1938.10.1057/jors.2014.104
   Web of Science ®Google Scholar
• Verma, H., Agrawal, R. K., & Sharan, A. (2016). An improved intuitionistic fuzzy c-means clustering algorithm incorporating local information for brain image segmentation. Applied Soft Computing, 46(C), 543–557.10.1016/j.asoc.2015.12.022
   Google Scholar
• Vernon, P. E. (1950). An application of factorial analysis to the study of test items. British Journal of Statistical, Psychology, 3(1), 1–15.10.1111/bmsp.1950.3.issue-1
   Google Scholar
• Wakker, P. (1999). Additive representations of preferences. Dordrecht: Kluwer Academic Publishers.
   Google Scholar
• Wang, J. Q., & Wang, R. Q. (2008). Hybrid random multi-criteria decision-making approach with incomplete certain information. Control and Decision Conference, 17(1), 1444–1448.
   Google Scholar
• Wang, W., & Liu, X. (2011). Intuitionistic fuzzy geometric aggregation operators based on Einstein operations. International Journal of Intelligent Systems, 26(11), 1049–1075.10.1002/int.v26.11
   Web of Science ®Google Scholar
• Wang, W., & Liu, X. (2012). Intuitionistic fuzzy information aggregation using Einstein operations. IEEE Transactions on Fuzzy Systems, 20(5), 923–938.10.1109/TFUZZ.2012.2189405
   Web of Science ®Google Scholar
• Wang, Z., Xu, Z., & Liu, S. (2011). A netting clustering analysis method under intuitionistic fuzzy environment. Applied Soft Computing, 11(8), 5558–5564.10.1016/j.asoc.2011.05.004
   Google Scholar
• Wang, Z., Xu, Z., & Liu, S. (2014). Direct clustering analysis based on intuitionistic fuzzy implication. Applied Soft Computing, 23(5), 1–8.
   Google Scholar
• Wei, G. W., & Zhao, X. F. (2012). Some induced correlated aggregating operators with intuitionistic fuzzy information and their application to multiple attribute group decision making. Expert Systems with Applications, 39(2), 2026–2034.10.1016/j.eswa.2011.08.031
   Web of Science ®Google Scholar
• Xia, M. M., Xu, Z. S., & Zhu, B. (2012). Some issues on intuitionistic fuzzy aggregation operators based on Archimedean t-conorm and t-norm. Knowledge-Based Systems, 31(7), 78–88.10.1016/j.knosys.2012.02.004
   Google Scholar
• Xu, J., Wan, S. P., & Dong, J. Y. (2016). Aggregating decision information into Atanassov’s intuitionistic fuzzy numbers for heterogeneous multi-attribute group decision making. Applied Soft Computing, 41(1), 331–351.10.1016/j.asoc.2015.12.045
   Google Scholar
• Xu, Z. S. (2004). Uncertain multiple attribute decision making: Methods and applications. Beijing: Tsinghua University Press.
   Google Scholar
• Xu, Z. S. (2007). Intuitionistic fuzzy aggregation operations. IEEE Transactions on Fuzzy Systems, 15, 1179–1187.
   Web of Science ®Google Scholar
• Xu, Z. S. (2009). A method based on the dynamic weighted geometric aggregation operator for dynamic hybrid multi-attribute group decision making. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 17(1), 15–33.10.1142/S0218488509005711
   Web of Science ®Google Scholar
• Xu, Z. S. (2010). Choquet integrals of weighted intuitionistic fuzzy information. Information Sciences, 180(5), 726–736.10.1016/j.ins.2009.11.011
   Web of Science ®Google Scholar
• Xu, Z. S., & Da, Q. L. (2002). The ordered weighted geometric averaging operators. International Journal of Intelligent System, 17(7), 709–716.10.1002/(ISSN)1098-111X
   Web of Science ®Google Scholar
• Xu, Z. S., & Xia, M. M. (2011). Induced generalized intuitionistic fuzzy operators. Knowledge-Based Systems, 24(2), 197–209.10.1016/j.knosys.2010.04.010
   Web of Science ®Google Scholar
• Xu, Z. S., & Yager, R. R. (2006). Some geometric aggregation operators based on intuitionistic fuzzy sets. International Journal of General Systems, 35(4), 417–433.10.1080/03081070600574353
   Web of Science ®Google Scholar
• Xu, Z. S., & Yager, R. R. (2009). Intuitionistic and interval-valued intutionistic fuzzy pref-erence relations and their measures of similarity for the evaluation of agreement within a group. Fuzzy Optimization and Decision Making, 8(1), 123–139.10.1007/s10700-009-9056-3
   Google Scholar
• Yang, W., & Chen, Z. (2012). The quasi-arithmetic intuitionistic fuzzy OWA operators. Knowledge-Based Systems, 27(3), 219–233.10.1016/j.knosys.2011.10.009
   Google Scholar
• Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353.10.1016/S0019-9958(65)90241-X
   Google Scholar
• Zeeny, M. (1982). Multiple criteria decision making. New York, NY: McGraw-Hill.
   Google Scholar
• Zhao, H., Xu, Z., & Liu, S. (2012). Intuitionistic fuzzy MST clustering algorithms. Computers & Industrial Engineering, 62(4), 1130–1140.10.1016/j.cie.2012.01.007
   Web of Science ®Google Scholar
• Zhao, X. F., & Wei, G. W. (2013). Some intuitionistic fuzzy Einstein hybrid aggregation operators and their application to multiple attribute decision making. Knowledge-Based Systems, 37(2), 472–479.10.1016/j.knosys.2012.09.006
   Google Scholar