We explore a variable-absent fuzzy relation inequality system with the Ordered Weighted Averaging (OWA) operator as a versatile substitute for max–min composition. Through dynamic weighting, OWA provides enhanced adaptability and decision-making ease in dynamic environments, e.g., multimedia streaming, where it resists server breakdowns and varying client needs. Substituting static max–min composition with OWA-based aggregation provides an optimal balance between adaptability and efficiency. We introduce a new 2-D path resolution technique designed specifically for OWA composition, maximizing computational efficiency and precision. We also pose a weighted optimization problem that seeks to minimize operating costs while ensuring service quality, taking advantage of OWA’s high weight dispersion. Empirical findings prove that OWA-based optimization decreases operational expenses by 15–20%, improves service quality, and attains near-linear execution scaling to 100 servers. Our efficient algorithm, tested through a real-world multimedia streaming scenario, highlights OWA’s greater flexibility in coping with uncertainties. Beyond streaming multimedia, our approach generalizes network optimization, supply chain management, and knowledge-based decision support systems with extensive real-world applications. This work contributes to the modeling and optimization of fuzzy relation systems in decision-making with uncertainty, proposing a scalable yet resilient method to suit dynamic contexts.