Bi-level data-driven enterprise-wide optimization with mixed-integer nonlinear scheduling problems

• Published in: Digital Chemical Engineering Vol. 14; p. 100218
• Main Authors: Nikkhah, Hasan, Aghayev, Zahir, Shahbazi, Amir, Charitopoulos, Vassilis M., Avraamidou, Styliani, Beykal, Burcu
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier Ltd 01.03.2025; Elsevier
• Subjects: Bi-level programming; Data-driven optimization; Integrated planning and scheduling; Mixed-integer nonlinear programming; Integrated planning and scheduling; Bi-level programming; Mixed-integer nonlinear programming; Data-driven optimization
• DOI: 10.1016/j.dche.2025.100218
• ISSN: 2772-5081

Planning and scheduling are crucial components of enterprise-wide optimization (EWO). For the successful execution of EWO, it is vital to view the enterprise operations as a holistic decision-making problem, composed of different interconnected elements or layers, to make the most efficient use of resources in process industries. Among different layers of the operating decisions, planning and scheduling are often treated sequentially, leading to impractical solutions. To tackle this problem, integrated approaches, such as bi-level programming are utilized to optimize these two layers simultaneously. Nonetheless, the bi-level optimization of such interdependent and holistic formulations is still difficult, particularly when dealing with mixed-integer nonlinear programming (MINLP) problems, due to a lack of effective algorithms. In this study, we employ the Data-driven Optimization of bi-level Mixed-Integer NOnlinear problems (DOMINO) framework, a data-driven algorithm developed to handle single-leader single-follower bi-level mixed-integer problems, to solve single-leader multi-follower planning and scheduling problems subject to MINLP scheduling formulations. We apply DOMINO to the continuous production of multi-product methyl methacrylate polymerization process formulated as a Traveling Salesman Problem and demonstrate its capability in achieving near-optimal guaranteed feasible solutions. Building on this foundation, we extend this strategy to solve a high-dimensional and highly constrained nonlinear crude oil refinery operation problem that has not been previously tackled in this context. Our study further evaluates the efficacy of using local, NOMAD (Nonlinear Optimization by Mesh Adaptive Direct Search), and a global data-driven optimizer, ARGONAUT (AlgoRithms for Global Optimization of coNstrAined grey-box compUTational), within the DOMINO framework and characterize their performance both in terms of solution quality and computational expense. The results indicate that DOMINO-NOMAD consistently achieves superior performance compared to DOMINO-ARGONAUT by identifying lower planning costs and generating more feasible solutions across multiple runs. Overall, this study demonstrates DOMINO’s ability to optimize production targets, meet market demands, and address large-scale EWO problems.