A fuzzy multicriteria decision‐making methodology for selection of human resources in a Greek private bank

The purpose of this paper is to present a fuzzy multicriteria decision‐making (MCDM) methodology for selecting employees.

The methodology is based on the technique for order preference by similarity to ideal solution (TOPSIS) multicriteria decision tool and the algorithm presented by Karsak. Assuming that n are candidates each of whom is evaluated in j criteria, the methodology starts by defining the ideal and the anti‐ideal candidate.

The applicability of the methodology is discussed using real data from a major Greek bank. As a result, it is necessary to consider criteria, criteria weights, and the distances from both the ideal and the anti‐ideal solution in order to select the more appropriate candidate.

Modern approaches recognize that selection of human resources is a complex process that involves a significant amount of vagueness and subjectivity, and serious consideration for candidate's uncertainties of career life.

The method can help human resources managers reach better decisions by selecting employees through a process that takes into account organizational objectives as well as employees' qualities. Moreover, selection of human resources can be seen as part of an integrated career management system in the organization.

The MCDM methodology can adequately represent the imprecision and uncertainty that are inherent in any modern organization. The method is quite flexible since criteria weights and distances from ideal and anti‐ideal candidates can be replaced by any method for ranking fuzzy numbers.