A Hyperheuristic Approach to Multi-Echelon Hub and Routing Optimization: Model, Valid Inequalities, and Case Study

Abstract

Efficient logistics management is critical in the modern global supply chain, and this study introduces an advanced hyperheuristic approach to the Multi-Echelon Hub and Routing Optimization (MEHRO) problem. The MEHRO problem encompasses optimizing hub locations and vehicle routes while balancing cost efficiency, service quality, and environmental sustainability. A novel mathematical model integrates transportation, hub setup, and inventory costs, strengthened by valid inequalities to enhance computational efficiency. The hyperheuristic framework dynamically selects from a pool of low-level heuristics, adapting strategies to varying problem instances. A real-world case study validates the model’s effectiveness, demonstrating significant cost reductions, improved service levels, and minimized environmental impact compared to traditional methods. This work sets a foundation for scalable and adaptive solutions in logistics and combinatorial optimization, catering to the evolving demands of global supply chain management.