Several types of crude oils arrive at inland oil refineries to be transformed into different intermediate and finished products. Incoming crude oils should be cleverly blended to meet some quality specifications before processing them in the crude distillation units (CDUs). This requires a careful allocation of the available crude oils to the refinery tanks and a proper sequence of the tanks feeding the same CDU. This work introduces a mixed-integer nonlinear programming (MINLP) formulation that uses general-precedence sequencing variables to choose the best ordering of operations in every tank. By using a rigorous objective function, the MINLP model provides the exact operating cost of the best solution found. The replacement of nonlinear component balances by tailor-made linear constraints in the MINLP leads to a tight mixed-integer linear (MILP) model that usually yields a good MINLP feasible point. A nonlinear programming (NLP) formulation that results by fixing the 0-1 variables to their MILP-values is subsequently solved to get a near-global optimal schedule. The proposed approach has been successfully applied to different instances of eight examples, four of which are benchmark problems already solved in previous contributions.