Superstructure hybrid optimization framework: An innovative approach for multi-objective supply chain optimization

Modeling process systems is highly complex and requires advanced tools to generate optimal solutions effectively. While no perfect mathematical model exists, integrating artificial intelligence with mathematical optimization helps overcome the limitations of traditional models. This paper presents a novel approach to supply chain optimization by incorporating machine learning models directly into optimization frameworks. Unlike traditional methods that require complex reformulations for integrating advanced machine learning algorithms, our strategy allows seamless incorporation into both deterministic and metaheuristic techniques, simplifying hybrid model implementation. To evaluate the proposed strategy, we analyze the installation of bioethanol biorefineries in the Michoacán region, Mexico, a major sugarcane producer. This case study leverages well-established processes to generate high-quality data for training machine learning models that enhance predictive accuracy. Additionally, it enables a comprehensive economic and environmental assessment of bioethanol production, highlighting its role in energy security, greenhouse gas reduction, and rural economic development. Results show that using a linear regression model and a neural network model yields an annual profit of 2.55 MM USD while minimizing costs and environmental impact. Although predictive models do not fully replace process simulators, our approach demonstrates that decision-making can significantly benefit from hybrid models, reducing computational complexity while maintaining accuracy.