Robust optimization for supply chain with routing problem: a learning-based approach

Effective supply chain management is crucial for businesses to remain competitive in today’s dynamic market. Despite extensive research, there is a lack of integrated approaches that simultaneously address resource allocation, routing, and delivery scheduling under uncertain conditions. 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’s capability to adapt to parameter variations. These findings emphasize the value of incorporating learning-based strategies into supply chain optimization, offering a powerful tool for organizations to address uncertainty and enhance decision-making efficiency.