A Comparative Analysis of CE-Topsis and CE-Maut Methods

A Comparative Analysis of CE-Topsis and CE-Maut Methods

Hakan Altin (University of Aksaray, Turkey)
Copyright: © 2020 |Pages: 34
DOI: 10.4018/IJSDS.2020070102
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Abstract

The key objective of this study is to conduct a comparative analysis of performance ranking results of the two multi-criteria decision-making methods, namely TOPSIS and MAUT. In this study, the CRITIC and ENTROPY methods were utilized as objective weighting techniques. In the application part of this study, three salient findings were attained. The first finding was that of the close relationship between TOPSIS ranking conducted by CRITIC and ENTROPY methods. The second finding was that of the close relationship between the MAUT ranking conducted by the CRITIC and ENTROPY methods. The third finding was that of the mutual and significant relationship in a positive direction between the performance ranking results obtained by TOPSIS and MAUT methods. In other words, TOPSIS and MAUT methods give the same performance ranking results. The results found are statistically significant.
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Literature Review

Opricovic and Tzeng (2004) demonstrated by comparative analysis the similarities and differences in VIKOR and TOPSIS, which are among the MCDM methods. Both methods are structured on a summation function representing the proximity to the ideal. The VIKOR method provides a ranking index that is based on proximity criterion to ideal solution. The main principle of the TOPSIS method is that the selected alternative must contain the shortest distance from the ideal solution and the longest distance from the negative ideal solution. The TOPSIS method presents two reference points but ignores the relative value of distances from these points. The study revealed that these two methods utilized different normalizations and provided varied summation functions for ranking.

Jahanshahloo, Lotfi and Izadikhah (2006) utilized the extended TOPSIS method in complex decision-making processes. The TOPSIS approach, a favorite multi-attribute model in making complex decisions, was analyzed as the ideal solution. The problem of decision making is the process of finding the optimum alternative among the all applicable options. In the real world, data are generally not very determinant since there is always some missing or unstated information because data are fuzzy and inconclusive. In this study, the TOPSIS method was harnessed as a solution method for decision-making problems with fuzzy data. Accordingly, the shorter the distance of the examined alternative from the fuzzy positive ideal solution and from the fuzzy negative ideal solution, the better ranking it has.

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