TOPSIS Approach Using Interval Neutrosophic Sets for Personnel Selection

Background and Objective: Personnel selection is one of the most critical strategies for companies to enhance the input quality of human resource their competitive strength in knowledge economy environment. Interval neutrosophic set (INS) is an adequate way for modeling uncertainty in decision making problems. Although numerous TOPSIS methods have been developed for personnel selection, none of them have used INS in their calculation. Therefore, this study integrated the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and INS to support for personnel selection. Materials and Methods: The proposed TOPSIS method is applied for selecting academic staffs at Academy of Finance. In the proposed TOPSIS approach, the ratings of alternatives and importance weights of criteria for personnel selection are represented by the INS. Results: Four academic staffs have been selected under six criteria including number of publications, quality of publications, personality factors, activity in professional society, classroom teaching experience and fluency in a foreign language. The results indicate that the best staff is A 2 which has highest closeness coefficient value. Conclusion: This study has developed the TOPSIS method using the information-centric network (ICN) in order to solve the personnel selection problem. The proposed method may also be applied to solve other multi-criteria decision making (MCDM) problems such as supplier selection, market segment selection and investor selection.