Scheduling product batches in pipelines is a very complex task with many constraints to he considered. Several papers have been published on the subject during the last decade. Most of them are based on large-size MILP discrete time scheduling models whose computational efficiency greatly diminishes for rather long time horizons. Recently, an MILP continuous problem representation in both time and volume providing better schedules at much lower computational cost has been published. However, all model-based scheduling techniques were applied to examples assuming static market environment, a short single-period time horizon and a unique due-date for all deliveries at the horizon end. In contrast, pipeline operators generally use a monthly planning horizon divided into a number of equal-length periods and a cyclic scheduling strategy to fulfill terminal demands at period ends. Moreover, the rerouting of shipments and time-dependent product requirements at distribution terminals force the scheduler to continuously update pipeline operations. To address such big challenges facing the pipeline industry, this work presents an efficient MILP continuous-time framework for the dynamic scheduling of pipelines over a multiperiod moving horizon. At the completion time of the Current period, the planning horizon moves forward and the re-scheduling process based on updated problem data is triggered again over the new horizon. Pumping runs may extend over two or more periods and a different sequence of batches may be injected at each one. The approach has successfully solved a real-world pipeline scheduling problem involving the transportation of four products to five destinations over a rolling horizon always comprising four 1-week periods. (C) 2007 Elsevier Ltd. All rights reserved.