Learning to Configure Hyperparameters in Solving Unit Commitment Problems with Mixed Integer Linear Programming Solvers

Unit commitment problems can be solved more efficiently with mixed integer linear programming solvers when more preferred hyperparameters are configured. We propose a learning approach to configure hyperparameters automatically for unit commitment problems. Our method employs a bipartite graph to re...

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Vydáno v:2025 5th International Conference on Artificial Intelligence and Industrial Technology Applications (AIITA) s. 1 - 6
Hlavní autoři: Ma, Zhoujun, Liu, Jinbo, Wang, Yishen, Wei, Renjie, Xue, Fei, Pan, Zhaoye, Yu, Yuanxin, Wang, Mengchang
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 28.03.2025
Témata:
Bipartite graph
Graph Convolutional Network
Graph convolutional networks
Mixed integer linear programming
Mixed Integer Programming
Navigation
Optimization
Power system dynamics
Predictive models
Propulsion
Refining
Solver Hyperparameters
Tuning
Unit Commitment
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Shrnutí:Unit commitment problems can be solved more efficiently with mixed integer linear programming solvers when more preferred hyperparameters are configured. We propose a learning approach to configure hyperparameters automatically for unit commitment problems. Our method employs a bipartite graph to represent the underlying structure of MILP and leverages a Graph Convolutional Network to adjust solver parameters on an instance-by-instance basis. The effectiveness of this approach is empirically validated through a series of numerical experiments, which demonstrate significant improvements in solver performance, resulting in noticeable computational time savings. These findings highlight the practicality and potential of our strategy in real-world applications of unit commitment, suggesting a promising new direction for optimization solver configuration.
DOI:10.1109/AIITA65135.2025.11047808