TY - JOUR
T1 - Learning to Recycle Benders Cuts for Mixed Integer Model Predictive Control
AU - Mitrai, Ilias
AU - Daoutidis, Prodromos
N1 - Publisher Copyright:
© 2024 Elsevier B.V.
PY - 2024/1
Y1 - 2024/1
N2 - Mixed integer MPC problems arise frequently in cases where the operation of a system depends on continuous and discrete decisions, leading to mixed integer optimization problems. However, the online solution of such problems is computationally challenging. In this work, we develop a machine learning approach to determine which cuts should be used as a warm start (recycled) for Generalized Benders Decomposition for the solution of mixed integer MPC problems. Computational results on a case study regarding the operation of chemical processes show that the proposed approach leads to a significant reduction in solution time (up to 40% reduction).
AB - Mixed integer MPC problems arise frequently in cases where the operation of a system depends on continuous and discrete decisions, leading to mixed integer optimization problems. However, the online solution of such problems is computationally challenging. In this work, we develop a machine learning approach to determine which cuts should be used as a warm start (recycled) for Generalized Benders Decomposition for the solution of mixed integer MPC problems. Computational results on a case study regarding the operation of chemical processes show that the proposed approach leads to a significant reduction in solution time (up to 40% reduction).
KW - Benders Decomposition
KW - Machine Learning
KW - Mathematical Optimization
KW - Model Predictive Control
UR - http://www.scopus.com/inward/record.url?scp=85196813320&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85196813320&partnerID=8YFLogxK
U2 - 10.1016/b978-0-443-28824-1.50278-7
DO - 10.1016/b978-0-443-28824-1.50278-7
M3 - Article
AN - SCOPUS:85196813320
SN - 1570-7946
VL - 53
SP - 1663
EP - 1668
JO - Computer Aided Chemical Engineering
JF - Computer Aided Chemical Engineering
ER -