Publications

Journal Publications

[10]. Mitrai, I., and Daoutidis, P., 2023, Taking the human out of the decomposition-based optimization via artificial intelligence Part II: Learning to initialize, Computers & Chemical Engineering, p. 108686  [arXiv][paper]

[9]. 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 & Chemical Engineering, p. 108688 [arXiv][paper]

[8]. Mitrai, I. and Daoutidis, P., 2024. Computationally efficient solution of mixed integer model predictive control problems via machine learning aided Benders Decomposition. Journal of Process Control, 137, p.103207. [arXiv][paper]

7. Mitrai, I., Palys, M., and Daoutidis, P., 2024. A two-stage stochastic programming approach for the design of renewable ammonia supply chain networks, Processes, 12(2), [paper]

6. Mitrai, I., Jones, O. V., Dewantoro, H., Stamoulis, C., and Daoutidis, P., 2022, Internal Control of Brain Networks via Sparse Feedback. AIChE Journal, p.e18061. [paper]

5. Palys M., Mitrai, I., and Daoutidis, P., 2023, Renewable hydrogen and ammonia for combined heat and power systems in remote locations: Optimal design and scheduling, Optimal Control Applications and Methods, https://doi.org/10.1002/oca.2793 [paper]

4. Mitrai, I., and Daoutidis, P., 2022, A multicut Generalized Benders Decomposition approach for the integration of process operations and dynamic optimization for continuous systems, Computers and Chemical Engineering, p.107859 [paper]

3. Mitrai, I., Tang, W. and Daoutidis, P., 2022, Stochastic Blockmodeling for Learning the Structure of Optimization Problems, AIChE Journal, DOI: 10.1002/aic.17415l. [paper]

2. Mitrai, I., and Daoutidis, P., 2021, Efficient Solution of Enterprise-wide Optimization Problems Using Nested Stochastic Blockmodeling, Industrial & Engineering Chemistry Research, 60(40), pp.14476-14494. [paper]

1. Mitrai, I., and Daoutidis, P., 2020, Decomposition of integrated scheduling and dynamic optimization problems using community detection, Journal of Process Control, 90, 63-74. [paper]

Conference Proceedings (peer-reviewed)

8. Mitrai, I., Palys, M., and Daoutidis, P., 2023, Optimal transition for ammonia supply chain networks via stochastic programming, Accepted to Foundations of Computer Aided Process Design (FOCAPD) 

7. Mitrai, I., and Daoutidis, P.,  2024, Learning to recycle Benders cuts for mixed integer model predictive control, Accepted to the 2024 European Symposium on Computer Aided Process Engineering.

6. Mitrai, I., and Daoutidis, P., 2024, Machine Learning-Based Initialization of Generalized Benders Decomposition for Mixed Integer Model Predictive Control, Accepted to the 2024 Americal Control Conference. 

5. Tang, W., Allman, A., Mitrai, I. and Daoutidis, P., 2023, 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]

4. 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]

3. 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]

2. Mitrai, I. and Daoutidis, P., 2021, An adaptive multi-cut decomposition-based algorithm for integrated closed loop scheduling and control, Computer Aided Chemical Engineering (Vol. 49, pp. 475-480). Elsevier. [paper]

1. Mitrai, I., Stamoulis, C., and Daoutidis, P., 2021, May. A sparse H∞ controller synthesis perspective on the reconfiguration of brain networks, 2021 American Control Conference ACC, pp. 1204-1209. IEEE. [paper]