SOWA Method Framework: New Algorithm for Criteria Weight Balancing with a Hybrid Subjective and Objective Approach

This research proposes the implementation of the subjective and objective weighting approach (SOWA) method as a new approach in determining the criteria weights that combines subjective assessments from experts and data-driven objective calculations. The criteria weights generated from the SOWA method are then used in various multi-criteria decision-making (MCDM) methods, such as simple additive weighting (SAW), technique for order preference by similarity to ideal solution (TOPSIS), multi-objective optimization on the basis of ratio analysis (MOORA), grey relational analysis (GRA), multi-attribute utility theory (MAUT), weighted aggregated sum product assessment (WASPAS), weighted product (WP), simple multi-attribute rating technique (SMART), multi-attributive border approximation area comparison (MABAC), and Multi-Attribute Ideal-Real Comparative Analysis (MAIRCA), to evaluate and rank alternatives. The research results show that the SOWA method is capable of producing balanced and representative weights, as well as consistent alternative rankings across MCDM methods. Sensitivity analysis of the ranking results indicates that all methods yield identical ranking results, signifying a high level of stability and reliability of the generated weights. These findings demonstrate that the SOWA method can serve as a solid foundation in decision support systems, particularly in the context of candidate selection or evaluation based on multiple criteria.