This thesis considers a cooperative routing problem, in which trucks and multiple drones serve a set of customers collaboratively. A truck can operate as a drone station that dispatches and collects multiple drones for nearby customers to overcome the drone's short operation range. Each customer has a time window, so either a truck or drone must serve the customer within the time window. Contrary to the drones, the trucks are subject to traffic conditions, which results in unreliable travel times. We addressed the trucks' travel time uncertainty by adopting the robust optimization approach.We first present a compact mathematical formulation for the problem. Then, we develop a decomposition approach based on the branch-and-price framework. The column generation subproblem is further decomposed into two distinct optimization problems, which constitute a nested-decomposition approach. The results of numerical experiments including real-life benchmark instances show that the proposed algorithm outperforms the state-of-the-art MIP solver.