The field of logistics and supply chain management is currently moving towards more dynamic and adaptive solutions, incorporating techniques such as reinforcement learning and machine learning to improve efficiency and equity. Researchers are focusing on developing models that can handle uncertainty and stochastic dynamics, such as those found in real-world routing problems. Another key area of research is the optimization of storage and retrieval systems, with a focus on maximizing space utilization and minimizing rearrangement efforts. Notable papers in this area include: SVRPBench, which presents a realistic benchmark for stochastic vehicle routing problems and challenges the community to design solvers that can generalize beyond synthetic assumptions. Learning to Search for Vehicle Routing with Multiple Time Windows, which proposes a reinforcement learning-based adaptive variable neighborhood search method that achieves significant improvements in solution quality and computational efficiency. OTPTO, which introduces a joint product selection and inventory optimization approach that substantially enhances the full order fulfillment rate in fresh e-commerce front-end warehouses. Collaborative Last-Mile Delivery, which introduces a novel collaborative synchronized multi-platform vehicle routing problem with drones and robots and develops a scalable heuristic algorithm to provide efficient, near-optimal solutions.