Abstract
In multiple attribute group decision-making (MAGDM) problems, weights of decision-makers play a vital role. In this paper, we present a new approach for finding the weights for decision-making process based on singular perturbation problem in which decision-makers’ weights are completely unknown. The attribute weights are derived using the exact and numerical solution for reaction-diffusion type problem. For the decision-making process, we utilize a class of ordered weighted averaging (OWA) operator, and the newly calculated decision-maker weights are used in the computations of identifying the best alternative from the available alternatives. The feasibility of the proposed method is displayed through a numerical illustration, and comparison is made with existing ranking methods.
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References
Atanassov, K. : Intuitionistic fuzzy sets. Fuzzy Sets and Systems. (1986) https://doi.org/10.1016/0165-0114(89)90205-4
Atanassov, K. : More on intuitionistic fuzzy sets. Fuzzy Sets and Systems. (1989) https://doi.org/10.1016/0165-0114(89)90215-7
Chen, S. M. : Similarity measures between vague sets and between elements. IEEE Trans. Syst. Man Cybern. 27(1), 153–158 (1997)
Chen, S.M., Randyanto, Y. : A Novel Similarity Measure Between Intuitionistic Fuzzy Sets and its Applications. International Journal of Pattern Recognition and Artificial Intelligence. World Scientific Publishing Company. 27(7), 1350021–1350034 (34 pages) (2013)
Hong, D. H., Kim, C. : A note on similarity measures between vague sets and between elements. Inform. Sci. 115(1), 83–96 (1999)
Hung, W. L., Yang, M.S. : Similarity measures of intuitionistic fuzzy sets based on Hausdorff distance. Pattern Recogn. Lett. 25(14), 1603–1611 (2004)
Li, D-F., Chuntian, C. : New similarity measures of intuitionistic fuzzy sets and application to pattern recognitions. Pattern Recogn. Lett. 23(13), 221–225 (2002)
Li, D-F., Ye Y-F. : Interval-valued least square prenucleolus of interval-valued cooperative games and a simplified method. Operational Research. 18(1), 205–220 (2018)
Li, D-F., Wan, S-P. : Minimum Weighted Minkowski Distance Power Models for Intuitionistic Fuzzy Madm with Incomplete Weight Information. International Journal of Information Technology and Decision Making. 16(5), 1387–1408 (2017)
Liu, J-C., Li, D-F. : Correlations to TOPSIS-Based Nonlinear-Programming Methodology for Multiattribute Decision making With Interval-Valued Intuitionistic Fuzzy Sets. IEEE Trans. Fuzzy Systems. 26(1), 391 (2018)
Li, F., Xu, Z. Y. : Measures of similarity between vague sets. J. Software. 12(6), 922–927 (2001)
Li, L., Olson, D.L., Qin, Z. : Similarity measures between intuitionistic fuzzy (vague) sets: A comparative analysis. Pattern Recogn. Lett. 28(2), 278–285 (2007)
Malley, R. E. O. : Introduction to singular perturbations. Academic press New York (1974)
Matthews, S., O’Riordan, E., Shishkin, G.I. : A Numerical Method for a System of Singularly Perturbed Reaction-Diffusion Equations. Journal of Computational and Applied Mathematics. 145(1), 151–166 (2002)
Miller, J.J.H., O’Riordan, E., Shishkin, G.I. : Fitted numerical methods for singular perturbation problems. World scientific Publishing Co. Pvt. Ltd. (1996)
Mitchell, H. B. : On the Li, D-F., Chuntian similarity measure and its application to pattern recognition. Pattern Recogn. Lett. 24(16), 3101–3104 (2003)
Nayfeh, A.H. : Perturbation methods. John Wiley and sons Newyork (1973)
Paramasivam, M., Valarmathi, S., Miller, J.J.H.: Second Order Parameter-Uniform Convergence for a Finite Difference Method for a Singularly Perturbed Linear Reaction-Diffusion System. Math. Commun. 15(2), 587–612 (2010)
Robinson, J.P., Amirtharaj, E.C.H. : Contrasting Correlation Coefficient with Distance Measure in Interval Valued Intuitionistic Trapezoidal Fuzzy Numbers. International Journal of Fuzzy System Applications. 5(3), 42–76 (2016)
Robinson, J.P., Jeeva, S. : Mining Trapezoidal Intuitionistic Fuzzy Correlation Rules for Eigen Valued MAGDM Problems. International Journal of Control Theory and Applications. 9(7), 585–616 (2016)
Robinson, J.P., Jeeva, S. : Application of Jacobian & Sor Iteration process in Intuitionistic Fuzzy MAGDM Problems. Mathematical Sciences International Research Journal. 6(2), 130–134 (2017)
Ross, H.G., Stynes, M., Tobiska, L. : Numerical Methods for Singularly Perturbed Differential Equations. Springer-Verlag Newyork (1996)
Yager, R. R., Filev, D. P. : Induced ordered weighted averaging operators. IEEE Transactions on Systems, Man and Cybernetics. 29(1), 141–150 (1999)
Ye, J. : Cosine similarity measures for intuitionistic fuzzy sets and their applications. Math. Comput. Model. 53(1-2), 91–97 (2011)
Yu, G-F., Li, D-F. : Application of satisfactory degree to interval-valued intuitionistic fuzzy multi-attribute decision making. Journal of Intelligent and Fuzzy Systems. 32(1), 1019–1028 (2017)
Yu, G-F., Li., D-F., Qiu, J-M., Zheng X-X. : Some operators of intuitionistic uncertain 2-tuple linguistic variables and application to multi-attribute group decision making with heterogeneous relationship among attributes. Journal of Intelligent and Fuzzy Systems. 34(1), 599–611 (2018)
Zadeh, L. A. : Fuzzy Sets. Information and Control. 8(3), 338–356 (1965)
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Robinson, P.J., Indhumathi, M., Manjumari, M. (2019). Numerical Solution to Singularly Perturbed Differential Equation of Reaction-Diffusion Type in MAGDM Problems. In: Rushi Kumar, B., Sivaraj, R., Prasad, B., Nalliah, M., Reddy, A. (eds) Applied Mathematics and Scientific Computing. Trends in Mathematics. Birkhäuser, Cham. https://doi.org/10.1007/978-3-030-01123-9_1
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DOI: https://doi.org/10.1007/978-3-030-01123-9_1
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