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.
Highlights
A management system is the collection of many factors in the form of values and goals, regulations, and structures, as well as method and decision-making practices
Many of the available modifications of the TOPSIS method can be used [6,9] with various forms of data representation, for example fuzzy numbers (FN) [10,11,12,13], intuitionistic fuzzy sets (IFS) [14,15], hesitant fuzzy sets (HFS) [16,17], hesitant fuzzy N-soft sets [18], dual extended hesitant fuzzy sets (DEHFS) [19], probabilistic soft sets (PSS) [20], ordered fuzzy numbers (OFNs) [21,22], and interval data [23,24]
The results presented in the article indicate that the extension of the fuzzy TOPSIS method by a modified similarity aggregation method (SAM) procedure may give better results than the fuzzy TOPSIS method implementing aggregation using average value or min-max functions
Summary
A management system is the collection of many factors in the form of values and goals, regulations, and structures, as well as method and decision-making practices. In the case of many complex, real decision problems solved with the participation of a group of decision makers (experts), it is important to capture the uncertainty of opinions and preferences expressed [8] In such situations, many of the available modifications of the TOPSIS method can be used [6,9] with various forms of data representation, for example fuzzy numbers (FN) [10,11,12,13], intuitionistic fuzzy sets (IFS) [14,15], hesitant fuzzy sets (HFS) [16,17], hesitant fuzzy N-soft sets [18], dual extended hesitant fuzzy sets (DEHFS) [19], probabilistic soft sets (PSS) [20], ordered fuzzy numbers (OFNs) [21,22], and interval data [23,24]. The article ends with conclusions and an indication of further research directions
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