An extended MABAC for multi-attribute decision making using trapezoidal interval type-2 fuzzy numbers
This work addresses uncertainty in decision-making problems, such as hiring in software companies, but is incremental as it adapts an existing method to a specific fuzzy number type.
The paper extended the MABAC method for multi-attribute decision making using interval type-2 trapezoidal fuzzy numbers to handle uncertainties, and demonstrated its validity through a candidate selection example with comparisons to existing methods.
In this paper, we attempt to extend Multi Attributive Border Approximation area Comparison (MABAC) approach for multi-attribute decision making (MADM) problems based on type-2 fuzzy sets (IT2FSs). As a special case of IT2FSs interval type-2 trapezoidal fuzzy numbers (IT2TrFNs) are adopted here to deal with uncertainties present in many practical evaluation and selection problems. A systematic description of MABAC based on IT2TrFNs is presented in the current study. The validity and feasibility of the proposed method are illustrated by a practical example of selecting the most suitable candidate for a software company which is heading to hire a system analysis engineer based on few attributes. Finally, a comparison with two other existing MADM methods is described.