Reference : Combining Mixed Integer Programming and Supervised Learning for Fast Re-planning
Scientific congresses and symposiums : Paper published in a book
Engineering, computing & technology : Computer science
http://hdl.handle.net/2268/73648
Combining Mixed Integer Programming and Supervised Learning for Fast Re-planning
English
Rachelson, Emmanuel mailto [Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
Ben Abbes, Ala mailto [ > > ]
Diemer, Sébastien mailto [ > > ]
2010
Proceedings of the 22nd IEEE International Conference on Tools with Artificial Intelligence
Yes
No
International
22nd IEEE International Conference on Tools with Artificial Intelligence
27-29/10/2010
Université d'Artois
Arras
France
[en] Mixed Integer Programming ; Boosting ; Power Systems Planning ; Hybrid methods
[en] We introduce a new plan repair method for problems cast as Mixed Integer Programs. In order to tackle the inherent complexity of these NP-hard problems, our approach relies on the use of Supervised Learning method for the offline construction of a predictor which takes the problem’s parameters as input and infers values for the discrete optimization variables. This way, the online resolution time of the plan repair problem can be greatly decreased by avoiding a large part of the combinatorial search among discrete variables. This contribution was motivated by the large-scale problem of intra-daily recourse strategy computation in electrical power systems. We report and discuss results on this benchmark, illustrating the different aspects and mechanisms of this new approach which provided close-to-optimal solutions in only a fraction of the computational time necessary for existing solvers.
http://hdl.handle.net/2268/73648

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