Weighting personnel selection criteria and personnel selection for promotion by using multi criteria decision making methods

Personnel selection helps to determine the quality and productivity of personnel. Therefore, personnel selection which is a very crucial issue for both academicians and industrialists, has a great importance in the business world. The purpose of this thesis is to prioritize personnel selection criteria and to determine the best personnel using the integrated Consistent Fuzzy Preference Relations (CFPR)-Fuzzy Analytic Hierarchy Process (FAHP) methodology and the integrated FAHP-Grey Relational Analysis (GRA) methodology. Firstly a personnel selection problem, main and sub-criteria, alternatives are defined for a case study in Turkey. Personnel selection criteria are categorized according to 22 sub-criteria under 5 main criteria according to academicians and managers' opinions and 17 alternatives are determined according to opinions of the managers. Then these criteria and alternatives are weighted by 3 experts. In this case study, the firm intends to promote one of the engineers in the position of chief engineer. This problem is solved in 3 different ways. In the first solution, the best personnel selection criteria is determined by CFPR methodology. In the second solution, the integrated CFPR-FAHP method is used. The criteria prioritized by CFPR, then are integrated with an FAHP model to prioritize the personnel alternatives. In the third solution, the integrated FAHP-GRA method are used. The values obtained by FAHP model are defuzzified and integrated with the GRA method. The ranking of the alternatives is calculated using the integrated CFPR-FAHP method and the integrated FAHP-GRA method and the best-performing personnel is selected for promotion. With the help of these methodologies, employees find the opportunity to improve themselves; on the other hand, managers/human resources department can easily predict how to evaluate and promote employees. The main contribution of this study is to reduce judgments for preference matrix using the integrated CFPR-FAHP methodology, to reduce the data using the integrated FAHP-GRA methodology. As well as the results obtained from the integrated methods are consistent.