Mixed integer nonlinear programming techniques for the short term scheduling of oil refineries

In this work, models are proposed that rely on mixed-integer nonlinear programming and on the discrete representation of the time domain along the scheduling horizon. The proposed models contain raw-material supply conditions, demand for products, initial inventories and qualities, and capacity constraints for processing, storage and transfer. From these data, an optimization solver that relies on an implementation of the outer approximation method obtains a solution that supports the production scheduler. The models were developed and solved at a growing level of complexity. Real-world problems with a time horizon of two days were solved that resulted in models with over 1000 binary variables and 10000 constraints.