A novel method for solving vehicle-based inventory routing problems (IRPs) under realistic constraints is presented. First, we propose a preprocessing algorithm that reduces the problem size by eliminating customers and network arcs that are irrelevant for the current horizon. Second, we develop a decomposition method that divides the problem into two subproblems. The upper level subproblem considers a simplified vehicle routing problem to minimize the distribution cost while satisfying minimum demands, which are calculated based on consumption rate, initial inventory and safety stock. In the lower level, a detailed schedule with drivers is acquired using a continuous-time MILP model, by adopting the routes selected from the upper level. Finally, an iterative approach based on the upper and lower levels is presented, including the addition of different types of integer cuts and parameter updates. Different options of implementing this iterative approach are discussed, and computational results are presented. (C) 2017 Elsevier Ltd. All rights reserved.