This paper aims at the detailed scheduling of branched multiproduct pipeline networks with multiple sources and an output terminal. Nevertheless, the complexity of the pipeline structure, coupling of hydraulic and scheduling factors, impacts of physical properties of products, and simultaneous injections at different sources make it challenging to draw out the injection operations at each source. Based on a continuous-volume and discrete-time representation, this paper develops a MINLP model to find the optimal injection operations of all sources at minimum total operational cost, rigorously tracking batch migration and power consumption at each pipeline segment. A priority algorithm is presented to obtain a high quality solution of this large-scale and nonlinear scheduling problem. Finally, experimental results on two virtual pipeline networks and a real-world pipeline network in China are given to validate the proposed approach. (C) 2018 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.