VRP Variant

Vehicle Routing Problems (VRPs) are complex optimization challenges with broad real-world applications. Recent research focuses on developing generalizable neural solvers capable of handling diverse VRP variants, moving beyond single-task approaches. This involves exploring architectures like mixture-of-experts models and leveraging transfer learning techniques, such as pre-training on simpler problems and fine-tuning with adapter networks, to improve efficiency and generalization across different problem types. These advancements aim to create more robust and practical solutions for logistics and supply chain management.