We consider the complex and not-yet-studied issue of building the tactical plan of a two-tiered city logistics system while explicitly accounting for the uncertainty in the forecast demand. We describe and formally define the problem and then propose a general modeling framework, which takes the form of a two-stage stochastic programming formulation, the first stage selecting the first-tier service network design and the general workloads of the intertier transfer facilities, and the second stage determines the actual vehicle routing on the second tier as well as some limited adjustments of the first-stage service design decisions. Four different strategies of adapting the plan to the observed demand are introduced together with the associated recourse formulations. These strategies are then experimentally compared through an evaluation procedure that, based on Monte Carlo principles, mimics the decision process of a priori planning followed by repetitively applying the adjusted plan to the periods of the planning horizon. The performances of the city logistics system under the adjustment strategies are contrasted through performance measures relative to the costs of operating the system, including those of additional vehicle capacity and movements required when the plan does not provide sufficient transportation means, the utilization of the various types of vehicles, the intensity of the vehicle presence within the city, and the utilization of the intertier transfer facilities. The comparisons are discussed both based on the numerical figures obtained through simulation and from the point of view of managerial insights into the implication for managing city logistics physical and human resources. The analysis emphasizes the interest of flexibility in managing resources and operations for the overall performance of the system, discusses the associated trade-offs, and underlines the benefits of consolidation in terms of system efficiency and impact on the city. The comparisons also show that even when demand variability and management constraints are explicitly taken into account, our approach is still able to build good tactical plans.