Abstract: Rural last-mile logistics continue to pose significant challenges due to sparse population density, limited road infrastructure, and long travel distances. Unmanned aerial vehicles (UAVs), or drones, offer a promising complementary mode to conventional ground vehicles, yet existing vehicle–drone routing formulations seldom address the unique characteristics of rural contexts. We consider the case in which there are multiple trucks and multiple drones, where the drones are carried by, dispatched from, and retrieved to their respective truck, but these operations are permitted only at pre-designated synchronization hubs (e.g., spacious areas in rural regions where trucks temporarily stop as mobile hubs). Each truck may simultaneously deploy several identical drones, but must remain stationed until all drones return. We formulate a bi-objective mixed-integer quadratic programming problem: a time-centric objective that minimizes the squared penalty of delivery delays plus the overall route makespan, thereby promoting prompt service and reducing service-level disparities common in geographically isolated areas, and a cost-centric objective that captures operational costs. To address computational complexity, we propose to convert the original two-echelon logistic network into a transformed network represented by a strongly connected directed graph, so that the problem can be presented in a path-based formulation. We develop an exact algorithm based on the branch-price-and-cut framework, which outperforms the state-of-the-art solvers.