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Decision-making problems arise in a wide range of applications in chemical engineering such as design of materials, process design and synthesis, process control, production planning and scheduling, and supply chain management/ logistics. The efficient solution of such problems is necessary to solve problems related to industrial decarbonization and sustainability.
Selected Publications
Li, Z., Agyeman, B.T., Mitrai, I. and Daoutidis, P., 2025. Learning to control inexact Benders decomposition via reinforcement learning. Computers & Chemical Engineering, p.109461. [arXiv][paper]
Mitrai, I., and Daoutidis, P., 2023, Taking the human out of the decomposition-based optimization via artificial intelligence Part I: Learning when to decompose, Computers and Chemical Engineering, p. 108688 [arXiv][paper]
Mitrai, I., and Daoutidis, P., 2023, Taking the human out of the decomposition-based optimization via artificial intelligence Part II: Learning to initialize, Computers and Chemical Engineering, p. 108686 [arXiv][paper]
Mitrai, I., and Daoutidis, P., 2023, A graph classification approach to determine when to decompose optimization problems, in Computer Aided Chemical Engineering (Vol. 52, pp. 655-660). Elsevier. [paper]
Mitrai, I., and Daoutidis, P., 2022, Learning to Initialize Generalized Benders Decomposition via Active Learning, Foundations of Computer Aided Process Operations / Chemical Process Control 2023, San Antonio TX [paper]
Tang, W., Allman, A., Mitrai, I. and Daoutidis, P., 2023, May. Resolving large-scale control and optimization through network structure analysis and decomposition: A tutorial review. In 2023 American Control Conference (ACC) (pp. 3113-3129). IEEE. [paper]
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