A Consensus Measure of Expert Judgment in the Fuzzy TOPSIS Method

Abstract

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.

Fintech investments in European banks: a hybrid IT2 fuzzy multidimensional decision-making approach

“…Even though there are various methods such as MARCOS, VIKOR, and MABAC that can be considered for this purpose, we suggest the TOPSIS method is more suitable as it identifies distances to both positive and negative ideal solutions (Rani et al 2020;Dhiman and Deb 2020). As a result, it is very helpful to reach more effective results (Rouyendegh et al 2020;Ziemba et al 2020). Moreover, considering IT2 fuzzy sets provides some benefits.…”

Multi-Criteria Fuzzy Evaluation of the Planned Offshore Wind Farm Investments in Poland

“…To solve the decision problem, the TFNs-based fuzzy TOPSIS method proposed by Chen et al [28] and modified by Ziemba et al was used [27]. This method allows K experts to consider a multi-criteria decision problem composed of m alternatives and n criteria.…”

“…Criterion weights and evaluation of alternatives are defined using the 7-point linguistic scales presented in Figure 1. Weights and expert assessments are aggregated to the TFNs form, respectively = , , , and = , , , , using the modified Similarity Aggregation Method (m-SAM), developed by Ziemba et al and presented in [27].…”

“…The applied methods use very complex fuzzy arithmetic, which make them very computationally complicated and therefore can be treated by stakeholders as a "black box", which may undermine the confidence of decision makers in the recommendations obtained using these MCDA methods [23]. The fuzzy TOPSIS method is the extension of the TOPSIS method [24,25], which allows the capture of the uncertainty of the input data as well as conflicting criteria, that is much less complicated [26,27]. It is based on the trapezoidal fuzzy numbers (TFNs), and is therefore much less computationally complex, more transparent and explainable, giving results that are easier to interpret.…”

Comparative Analysis of Hybrid Fuzzy MCGDM Methodologies for Optimal Robot Selection Process

“…Joshi and Kumar [39] extended the TOPSIS MCDM method by introducing a Choquet integral operator for an interval-valued intuitionistic hesitant fuzzy set for the alternative ranking purpose. BWM, TOPSIS, and VIKOR are very practical methods that have wide areas of application and success in decision processes [40][41][42][43][44][45][46].…”