Hybrid Modification Preference Selection Index with Optimized Pairwise Ratio Analysis Method for Multi-Criteria Decision Making: An Integrated Weighting and Ranking Approach
Multi-criteria job candidate selection requires an objective and systematic approach because candidates are evaluated using multiple criteria with different levels of importance. This study proposes a hybrid Modification Preference Selection Index with Optimized Pairwise Ratio Analysis (MPSI–OPARA) framework that integrates objective criteria weighting and alternative ranking. MPSI is used to determine criterion weights based on normalized performance variation, while OPARA is applied to rank candidates through adjusted pairwise comparisons. The framework is demonstrated using six job candidates evaluated across six criteria: educational background, work experience, technical competence, communication skills, problem-solving ability, and interview performance. The results show that educational background and work experience are the dominant criteria, with weights of 0.5600 and 0.3422, respectively. OPARA produces Candidate IMS as the first-ranked candidate, Candidate GTB as the second-ranked candidate, and Candidate RDH as the third-ranked candidate. Furthermore, comparisons with SAW, SMART, WASPAS, and MOORA produce identical ranking positions, demonstrating consistent ranking outcomes. These findings indicate that the proposed MPSI–OPARA framework provides a systematic and consistent approach for objective criteria weighting and job candidate selection.
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