Multi-criteria IT personnel selection on intuitionistic fuzzy information measures and ARAS methodology

Global challenge and the speedy growth of information technologies compel organizations to constantly change their ways. At the present time, associations need IT personnel who create a difference by creative thoughts and who preserve with the rapid amendments. Since the evaluation of IT personnel selection (ITPS) consists of different alternatives and criteria, therefore, IT personnel selection could be regarded as a multi-criteria decision making (MCDM) problem. The doctrine of intuitionistic fuzzy sets (IFSs) is an effective tool to elucidate the uncertain information in an MCDM problem. The main objective of the paper is to choose the best IT personnel candidate by integrating intuitionistic fuzzy Additive Ratio Assessment (IF-ARAS) method with divergence measure, improved score function and IF-aggregation operators. In the developed methodology, the weights of criteria and decision experts (DEs) are computed based on proposed IF-divergence measure method intuitionistic fuzzy preference evaluation method, respectively. Next, the decision experts’ judgments are aggregated of the proposed method to evade the loss of data. Finally, the proposed IF-ARAS method is implemented to solve the IT-personnel selection (ITPS) problem to indicate the applicability of the presented approach. In addition, a comparative analysis is provided to discuss the obtained results for validating the developed methodology. The analysis illustrates that the IF-ARAS method is effective and well consistent with the existing ones.

Keywords:

Intuitionistic fuzzy sets , Divergence measure , personnel selection , Multi-criteria Decision Making , ARAS