Vehicle Routing Problems are among the most difficult combinatorial problems to solve to optimality. They were proposed in the late 1950 s and since then have been widely studied. This interest arises from their practical importance, as well as the difficulty of providing efficient algorithms to solve them. This thesis is mainly concerned with the exact resolution of the Capacitated Vehicle Routing Problem (CVRP). In this problem, a set of customers, each one associated to a demand, must be serviced by a fleet of vehicles. All vehicles have the same (limited) capacity and initially are located in the same central depot. A solution is a set of routes, starting and ending at the depot, that visit every customer exactly once. The only constraint on a route is that the sum of the demands of its customers does not exceed the vehicle capacity. The objective is to find a solution with minimum total cost. The best performing exact algorithms for the CVRP developed in the last 10 years are based in the combination of cut and column generation. Some authors only used cuts expressed over the variables of the original formulation, in order to keep the pricing subproblem relatively easy. Other authors could reduce the duality gaps by also using a restricted number of cuts over the Master LP variables, stopping when the pricing becomes prohibitively hard. A particularly effective family of such cuts are the Subset Row Cuts. This thesis introduces a technique for greatly reducing this impact on the pricing of these cuts, thus allowing much more cuts to be added. The newly proposed Branch-Cut-and-Price algorithm also incorporates and combines for the first time (often in an improved way) several elements found in previous works, like route enumeration, variable fixing and strong branching. All the instances used for benchmarking exact algorithms, with up to 199 customers, were solved to optimality. Moreover, some larger instances with up to 360 customers, only considered before by heuristic methods, were solved too.