Human Resources Manager Selection Based on Fuzzy and Intuitionistic Fuzzy Numbers for Logistics Companies

Qualified managers are needed for the systematic functioning of logistics management applications. In logistics companies, the human resources manager takes part in determining the amount of personnel needed, supplying personnel, and creating personnel task forms. For successful human resources management, a qualified human resources manager should be selected. In the literature, it is seen that the manager selection problem is handled with multi criteria decision making (MCDM) methods. The aim of this research is to determine the problem criteria of human resources manager selection for logistics companies and to apply them with hybrid MCDM methods. With the in-depth literature review, ten criteria were determined for the logistics company human resources manager selection problem. The intuitionistic fuzzy weighted averaging (IFWA) method was used to weight the criteria. The fuzzy multi attribute ideal-real comparative analysis (F-MAIRCA) method was applied for the ranking of the four candidate managers. The application was made on a logistics company operating in Turkey. Research methods are based on fuzzy and intuitionistic fuzzy numbers. As a result of the study, the most important criterion in the selection of logistics human resources manager was determined as the experience criterion. Among the four candidates, the first candidate was seen as the best manager candidate. Suggestions have been developed for logistics companies, human resources manager candidates and researchers based on the research outputs. In addition, with this research, the IFWA and F-MAIRCA hybrid method has been brought to the literature.