The aim of this Master's Degree Thesis is to propose a solution for rebalancing stocks among retail points of sale of one of the UK's leading retailers. Each day, a fleet of vehicles serves a set of locations with a variety of products. The fact that the vehicles follow pre-established routes leads to frequent lost sales among those points of sale that are later served. In this Thesis, an end-to-end solution is proposed to solve a variant of the VRP that takes into account both transportation and lost sales costs: - The dynamic and deterministic version of the problem is solved using exact formulations and metaheuristics (Genetic Algorithm and Greedy Randomized Adaptative Search Procedure). - A visualization tool is implemented combining R Shiny, AMPL and MySQL to show the behavior of the solution.