Scheduling and feed quality optimization for processing solid concentrates in the copper refining industry may be formulated as a large-scale, discrete-time, nonconvex mixed-integer nonlinear program (MINLP) by including logistics operations and ad-hoc blending constraints. However, to solve this complex problem, the full space MINLP for the blending of solid concentrates of copper and the scheduling of their logistics is partitioned into a mixed-integer linear program (MILP) and a nonlinear program (NLP). The solution strategy considers the relax-and-fix rolling horizon with nearby time window overlaps and the use of multiple MILP solutions applied in a two-step MILP-NLP procedure. Two models are proposed for the flowsheet balances: a split fraction model and a process network model. The results indicate that the split fraction model yields near optimal solutions with a large computational effort, whereas the process network can generate several feasible solutions faster. We present a motivating example and an industrial problem with MILP to NLP gaps close to 0%.