Abstract: With the increasing consumption and depletion of natural resources, the adoption of reverse logistics options has become increasingly necessary for all organizations. Furthermore, reverse logistics represents a source of competitive advantage in business markets. Managing e-commerce returns, waste materials, and other reverse flows in urban areas relies on determining the routes to be performed by a fleet of service vehicles. Specifically, many operational problems in reverse logistics can be studied as variants of the well-known Vehicle Routing Problem (VRP). This talk deals with uncertain reverse logistics VRPs in which some (or all) locations to be serviced are identified as pick-up points. These problems are solved by using stochastic optimization approaches, whereby parts of the input data are modelled as random variables that follow a given probability distribution. Tailored recourse strategies are presented in the solution process.