In this paper, a novel multi-attribute decision making (MADM) methodology is presented for evaluation and selection of the most suitable candidate for a software company which is heading to hire a system analysis engineer based on few attributes in fuzzy environment. A novel systematic assessment methodology is proposed by integrating trapezoidal interval type-2 fuzzy numbers (TrIT2FNs) based MABAC (Multi-Attributive Border Approximation area Comparison). Type-2 fuzzy sets involve more uncertainties than type-1 fuzzy sets. They provide us with additional degrees of freedom to represent the uncertainty and the fuzziness of the real world. TrIT2FNs- based MABAC evaluates the candidates considered for the job based on some attributes whose weights/priorities are fixed by a group of experts. The proposed model is validated through an well-known example and results are compared with two other MADM methods.
DONATE to arXiv: One hundred percent of your contribution will fund improvements and new initiatives to benefit arXiv's global scientific community. Please join the Simons Foundation and our generous member organizations and research labs in supporting arXiv. https://goo.gl/QIgRpr
from cs.AI updates on arXiv.org http://ift.tt/29gyNlN
via IFTTT
No comments:
Post a Comment