Soft computing-based preference selection index method for human resource management

Multiple Attributes Decision Making MADM is the process of finding the best candidate and involves the evaluation and selection among a finite number of potential candidates to solve real-life complex decision problems. In classical MADM methods, the relative importance of the conflicting criteria and performance ratings of candidates are determined precisely. However, in real-world systems related to human resource management, decision making problems are often uncertain or vague, and because of the lack of information, the future state of these systems cannot be known completely. Moreover, if decision makers cannot reach an agreement on the method of defining linguistic variables based on the traditional fuzzy sets, the Interval-Valued Fuzzy Sets IVFSs theory can provide a more accurate and practical modeling. This paper presents an Interval-Valued Fuzzy Preference Selection Index IVF-PSI method aiming at solving complex decision making problems, in which the performance ratings of candidates are described by using the concept of the IVFSs. Finally, the executive procedure of the proposed IVF-PSI method is illustrated by applying it to the expatriate selection process from the viewpoint of human resource managers.