This work introduces a mixed-integer nonlinear programming (MINLP) formulation for the efficient planning of crude oil supplies to a major refinery, through a long-distance pipeline. The proposed approach combines the potentials of slot-based and general precedence continuous-time representations to simultaneously address two problems that are typically decoupled: pipeline transportation and crude oil blending. General precedence sequencing variables coordinate incoming/outgoing flows to/from every tank, while crude oil batches are traced into the pipeline following a slot-based scheme. The model precisely monitors key component concentrations, keeping oil properties within admissible ranges. Since the proposed formulation is nonconvex, an efficient solution strategy is followed and a tailored relaxation is subsequently solved to assess the quality of the solutions achieved. Results show that the model is able to find very efficient solutions to real-world case studies in modest CPU times. (C) 2018 Elsevier Ltd. All rights reserved.