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Open AccessArticle

A Consensus Measure of Expert Judgment in the Fuzzy TOPSIS Method

1
Faculty of Economics, Finance and Management, University of Szczecin, 71-101 Szczecin, Poland
2
Faculty of Economics, West Pomeranian University of Technology, 71-210 Szczecin, Poland
3
Faculty of Technology, The Jacob of Paradies University, 66-400 Gorzów Wielkopolski, Poland
*
Author to whom correspondence should be addressed.
Symmetry 2020, 12(2), 204; https://doi.org/10.3390/sym12020204 (registering DOI)
Received: 31 December 2019 / Revised: 16 January 2020 / Accepted: 18 January 2020 / Published: 1 February 2020
In the case of many complex, real-world decision problems solved with the participation of a group of experts, it is important to capture the uncertainty of opinions and preferences expressed. In such situations, one can use many modifications of the technique for order preference by similarity to the ideal solution (TOPSIS) method, for example, based on fuzzy numbers. In fuzzy TOPSIS, two aggregation methods of fuzzy expert opinions dominate, the first based on the average value technique and the second one extended by the minimum and maximum functions for determining the support of the aggregated fuzzy number. An important disadvantage of both techniques is the fact that the agreement degree of expert opinions is not taken into account. This article proposes the inclusion of the modified procedure for aggregating individual expert opinions, taking into account the degree of agreement of their opinions (called the similarity aggregation method—SAM) and the ranking of experts into the fuzzy TOPSIS method. The fuzzy TOPSIS method extended in this way was used to solve the decision problem of recruiting employees by a group of experts. As part of the solution, the modified SAM was compared with aggregation procedures based on the average value and min-max (minimum and maximum) support. The results of the conducted research indicate that SAM allows fuzzy numbers to be obtained, characterized by less imprecision and greater stability than the other two considered aggregation procedures. View Full-Text
Keywords: multicriteria group decision making; fuzzy TOPSIS; similarity aggregation method; group decision support system; human resources management multicriteria group decision making; fuzzy TOPSIS; similarity aggregation method; group decision support system; human resources management
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Ziemba, P.; Becker, A.; Becker, J. A Consensus Measure of Expert Judgment in the Fuzzy TOPSIS Method. Symmetry 2020, 12, 204.

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