This work explores the application of Economic Model Predictive Control (EMPC) to process scheduling problems involving limited production resources and multiple products. EMPC extends traditional MPC by directly optimizing an economic cost function, aligning control objectives with operational profitability. The study implements both open-loop and closed-loop EMPC formulations, exemplifying the influence of prediction horizon length on closed-loop performance. Computational experiments are conducted on a representative production scheduling scenario, where production rates and inventory levels are optimized under resource and demand constraints. Conventional wisdom suggests that larger prediction horizons lead to improved closed-loop performance. However, this may not be true for production scheduling problems. In particular, significant degradation in performance can result from increasing horizon size by a single time unit. Strategies to mitigate horizon size sensitivity are currently being investigated.
