This work assesses robust optimization as a solution method for the combined issue of vehicle routing and inventory management, adding different characteristics. Thus, its behavior may resemble the industry’s current reality. This work also emphasizes the utilization of time windows, considers possible disruptions to the routes that connect the different nodes, and includes a Green Factor assessment. For these purposes, the minimax regret criteria is applied to a set of pre-established instances from the literature, adding the necessary information requirements to evaluate how the method behaves through different parameter combinations, thus changing the number of nodes, costs, and available alternatives and scenarios. The study compares the solutions achieved against the solutions from the classical model to assess the relation between the results from the exercise and the different parameter modifications implemented, achieving a 40% improvement in the total cost algorithm—proportional to the increase in the alternatives and scenarios assessed. The approach proposed allows us to create disruption scenarios for the distribution process, which are connected to the mathematical optimization problem that allows us to determine the best routing process given the uncertainty associated with the disruption events. Our results also allow us to analyze the trades off between the green factors when they are included in the objective function and the results without them.