<![CDATA[This study develops a unified robust stochastic optimization framework designed to enhance the stability, efficiency, and reliability of transportation models operating under significant uncertainty. Traditional deterministic, robust-only, and stochastic-only approaches each face limitations deterministic models fail under variability, robust models tend to be overly conservative, and stochastic models struggle under extreme disruptions. To address these gaps, the proposed framework integrates worst-case uncertainty sets with probabilistic scenario modeling, enabling decisions that remain feasible under extreme conditions while maintaining optimal performance during typical operations. The methodology includes comprehensive uncertainty modeling of travel time fluctuations, demand variability, cost changes, and network disruptions; a hybrid mathematical formulation combining robust constraints with stochastic scenarios; and an efficient algorithmic structure employing enhanced decomposition techniques and scenario filtering to reduce computational complexity. Experimental results using benchmark and real-world transportation datasets show significant improvements in solution stability, travel time reliability, cost efficiency, and network resilience compared with conventional models. The hybrid framework reduces over-conservatism, lowers operational cost by up to 25%, and increases robustness under high-variability conditions, demonstrating superior performance in both normal and disrupted environments. The study advances optimization theory by offering a scalable and computationally tractable integration of two major uncertainty-handling paradigms, while contributing to transportation modeling through a practical tool capable of supporting reliable routing, scheduling, and logistics planning. Overall, this research provides a robust and adaptive optimization strategy that strengthens decision-making under uncertainty and improves the resilience of modern transportation systems.]]>
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