With the rapid development of modern information technology, e-commerce has become an integral part of our daily life, which demands intelligent and highly efficient logistics systems. Currently, fixed partitioning methods are widely adopted. To optimize delivery efficiency, this work introduces a changing distribution partitioning network. The core of this network is the concept of reachable distance, whose use enables us to well balance delivery workloads of couriers. The optimization problem addressed in this work consists of two classic NP-hard problems: Distribution Partitioning and Vehicle Routing ones. To solve it, we propose a Simulated-Annealing-Assisted Artificial Hummingbird Algorithm (SAHA), such that its total transportation cost is minimized. Through experimental validation, we determine that setting the reachable distance to 40% of the distance to the nearest distribution center yields optimal or near-optimal performance. Compared to the traditional community logistics algorithm, SAHA reduces the total transportation distance by approximately 8% and shortens the convergence time by 59%. Additionally, we confirm the robustness of SAHA across both small-scale suburban and high-density urban environments. These results underscore the strong potential of SAHA to enhance operational efficiency and adaptability in smart logistics systems.