This dissertation addresses a generalization of the vehicle routing problem (VRP) that arises in real life applications in ports and mine operations. In this VRP variant, each customer may demand different types of vehicles to perform a task collaboratively. Vehicles are allowed to wait at the locations but they must start operating at the same time. The objective is to route the available vehicles while maximizing the (weighted) sum of served customers and minimizing the total distance traveled. The specific case where all customers must be served while minimizing the total distance traveled is the central problem here studied. This special case can be viewed as a straightforward generalization of, a well known and more specific routing problem, the VRP with time windows (VRTPTW) where the capacity of the vehicles is sufficiently large. We support this narrower scope by stating that it allows a clear comparison of the problem hardness by its relation to the VRPTW. Sticking to the classification of synchronization in vehicle routing proposed by (DREXL, 2012) we named this problem as the Vehicle Routing Problem with Time Windows and Exact Operation Synchronization (VRPTWEOS). In this work, a formal definition for the VRPTWEOS is provided. Integer programming models for this problem are proposed and analyzed. Furthermore, we propose a solution method based on the Dantzig-Wolfe decomposition for which exact and aproximated resolution algorithms are described. In order to test the performance of those algorithms, a group of benchmark instances for the VRPTWEOS was created on top of the Solomon benchmark for the VRPTW. The method used to create the benchmark instances is described in detail. Computational experiments over the mentioned set of instances showed that the proposed solution approach is a promising alternative for solving the VRPTWEOS.