<![CDATA[This study aims to analyze the application of the Multi-Objective Optimization on the basis of Ratio Analysis (MOORA) and the Multi-Objective Optimization by Simple Ratio Analysis (MOOSRA) methods across various studies in the field of computer science through a literature review approach. The analysis of several scientific publications reveals that both methods are widely used in administrative and semi-technical decision-support contexts, such as major selection, supplier evaluation, laptop selection, and digital platform assessment. Methodologically, the implementation patterns of MOORA and MOOSRA across studies show relatively similar stages, including constructing a decision matrix, performing normalization, assigning weights (optional), and calculating final scores based on benefit and cost attributes. MOORA determines scores by adding benefit values and subtracting cost values, whereas MOOSRA uses a ratio of benefit to cost, resulting in more stable and consistently positive outputs. The findings indicate that MOORA is more commonly used due to its simplicity and popularity, while MOOSRA tends to be chosen in cases requiring higher score stability and lower sensitivity to data variability. The review demonstrates that both methods provide clear and accurate ranking outcomes, although they each have limitations—for instance, the potential for negative scores in MOORA or the possibility of oversimplification in MOOSRA’s ratio-based approach. Overall, the study confirms that MOORA and MOOSRA are flexible, easy-to-apply MCDM methods that remain highly relevant for supporting multi-criteria decision-making. Furthermore, this research recommends deeper exploration in more technical domains of computer science to address existing gaps in the literature]]>
Copyrights © 2026
