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This study develops a hybrid framework that combines robust optimization, simulated annealing, and reinforcement learning to enhance supply chain operations in complex networks involving fixed suppliers, distribution centers, and customers. Empirical results from rigorous testing demonstrate significant efficiency improvements and adaptability across diverse scenarios. A real-world case study from the logistics sector highlights the practical benefits, achieving notable cost savings and operational robustness. Sensitivity analysis further underscores the model\u2019s capability to adapt to parameter variations. 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