A fuzzy MCDM approach for personnel selection


EXPERT SYSTEMS WITH APPLICATIONS, vol.37, no.6, pp.4324-4330, 2010 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 37 Issue: 6
  • Publication Date: 2010
  • Doi Number: 10.1016/j.eswa.2009.11.067
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.4324-4330
  • Keywords: Personnel selection, 2-Tuple linguistic representation, Ordered weighted averaging (OWA), TOPSIS, Decision making, GROUP DECISION-MAKING, LINGUISTIC REPRESENTATION MODEL, INFORMATION, TECHNOLOGY, SYSTEM, WORDS, SETS
  • Galatasaray University Affiliated: Yes


Many individual attributes considered for personnel selection such as organizing ability, creativity, personality, and leadership exhibit vagueness and imprecision. The fuzzy set theory appears as an essential tool to provide a decision framework that incorporates imprecise judgments inherent in the personnel selection process. In this paper, a fuzzy multi-criteria decision making (MCDM) algorithm using the principles of fusion of fuzzy information, 2-tuple linguistic representation model, and technique for order preference by similarity to ideal solution (TOPSIS) is developed. The proposed method is apt to manage information assessed using both linguistic and numerical scales in a decision making problem with multiple information sources. Furthermore, it enables managers to deal with heterogeneous information. The decision making framework presented in this paper employs ordered weighted averaging (OWA) operator that encompasses several operators as the aggregation operator since it can implement different aggregation rules by changing the order weights. The aggregation process is based on the unification of information by means of fuzzy sets on a basic linguistic term set (BLTS). Then, the unified information is transformed into linguistic 2-tuples in a way to rectify the problem of loss information of other fuzzy linguistic approaches. The computational procedure of the proposed framework is illustrated through a personnel selection problem reported in an earlier study. (C) 2009 Elsevier Ltd. All rights reserved.