<![CDATA[Computational-based decision-making processes generally face two major challenges: the vast solution search space, which makes exhaustive search inefficient, and the presence of uncertain or ambiguous information that is difficult to process using conventional computational methods. Genetic Algorithms (GA) are widely used to solve optimization problems in complex search spaces, while Fuzzy Logic is applied to handle uncertainty and linguistic information. However, studies that examine both methods within an integrated framework remain relatively limited. This study synthesizes nine national journal articles indexed by Sinta and one reference book on Fuzzy Logic. The review covers the application of Genetic Algorithms in route optimization problems, such as the Traveling Salesman Problem (TSP), multi-TSP, and goods distribution management, including their implementation in various scheduling cases. In addition, this study examines the application of Mamdani and Sugeno Fuzzy Logic in evaluation and classification processes, such as determining job priorities, identifying depression levels, and selecting products. Using a narrative-thematic literature review method, the study concludes that Genetic Algorithms are highly effective for problems involving combinational search with performance measures that can be objectively evaluated through fitness functions. On the other hand, Fuzzy Logic is more suitable for decision-making situations involving subjective assessments, linguistic terms, and unclear decision boundaries. The synthesis results indicate that both approaches can be positioned as complementary methods. Genetic Algorithms function as mechanisms for finding optimal solutions, while Fuzzy Logic serves as a tool for evaluating the quality and feasibility of those solutions. The integration of both methods has the potential to produce decision support systems that are more flexible and adaptive compared to using each method separately.]]>
