Personnel selection is a complex, multi-criteria decision-making problem that involves both qualitative and quantitative factors. Despite various techniques being proposed across different industries, a robust method that adequately addresses uncertainty remains needed. This study introduces an integrated approach, combining the Step-wise Weight Assessment Ratio Analysis (SWARA) and the Double Normalization-Based Multiple Aggregation (DNMA) methods. The proposed framework first employs the SWARA method to determine the importance of criteria, followed by the application of the DNMA method to rank candidates based on a sequential evaluation process.

The goal of the study is to select the most suitable candidate from five applicants for a vacant position in a company within the participatory software sector. The criteria weights were primarily determined by the decision-maker using the SWARA method, with computer and software skills, work experience, teamwork adaptability, foreign language proficiency, and problem-solving skills identified as the five key criteria. The DNMA method was then used to assess the candidates' performance, and the results indicated that one of the candidates was the best fit for the position.

When compared with similar studies in the literature, it was found that while many multi-criteria decision-making methods have been employed, the combination of SWARA and DNMA methods is novel. This study demonstrates the effectiveness of these methods in personnel selection, offering a new approach to the literature on decision-making processes.