T. Achterberg, T. Koch, M. , and A. , Branching rules revisited, Operations Research Letters, vol.33, issue.1, pp.42-54, 2005.

A. M. Alvarez, Q. Louveaux, and L. Wehenkel, A machine learning-based approximation of strong branching, INFORMS Journal on Computing, vol.29, issue.1, pp.185-195, 2017.

D. Applegate, R. Bixby, V. Chvatal, and W. Cook, Concorde tsp solver, 2006.

D. L. Applegate, R. E. Bixby, V. Chvatal, and W. J. Cook, The traveling salesman problem: a computational study, 2011.

A. Auger and B. Doerr, Theory of randomized search heuristics: Foundations and recent developments, vol.1, 2011.

S. Basso, A. Ceselli, and A. Tettamanzi, Random sampling and machine learning to understand good decompositions, 2017.

P. Bonami, A. Lodi, and G. Zarpellon, Integration of Constraint Programming, Artificial Intelligence, and Operations Research, pp.595-604, 2018.

M. Carrión and J. M. Arroyo, A computationally efficient mixed-integer linear formulation for the thermal unit commitment problem, IEEE Transactions on power systems, vol.21, issue.3, pp.1371-1378, 2006.

B. Cornelusse, G. Vignal, B. Defourny, and L. Wehenkel, Supervised learning of intra-daily recourse strategies for generation management under uncertainties, PowerTech, pp.1-8, 2009.

H. Dai, E. B. Khalil, Y. Zhang, B. Dilkina, and L. Song, Learning combinatorial optimization algorithms over graphs, 2017.

G. Dantzig, R. Fulkerson, J. , and S. , Solution of a large-scale traveling-salesman problem, Journal of the operations research society of America, vol.2, issue.4, pp.393-410, 1954.

K. J. Dembczynski, W. Cheng, and E. Hüllermeier, Bayes optimal multilabel classification via probabilistic classifier chains, Proceedings of the 27th International Conference on Machine Learning (ICML-10), pp.279-286, 2010.

M. Fischetti and M. Fraccaro, Machine learning meets mathematical optimization to predict the optimal production of offshore wind parks. Computers and Operations Research, 2018.

J. Forrest, Cbc (coin-or branch and cut) opensource mixed integer programming solver, 2012.

M. Gendreau and J. Potvin, Handbook of metaheuristics, vol.2, 2010.

H. He, I. Daume, H. Eisner, and J. M. , Learning to search in branch and bound algorithms, Advances in Neural Information Processing Systems, pp.3293-3301, 2014.

E. B. Khalil, P. Le-bodic, L. Song, G. Nemhauser, and B. Dilkina, Learning to branch in mixed integer programming, Proceedings of the 30th AAAI Conference on Artificial Intelligence, 2016.

M. Kruber, M. E. Lübbecke, and A. Parmentier, Learning when to use a decomposition, International Conference on AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp.202-210, 2017.

E. Larsen, S. Lachapelle, Y. Bengio, E. Frejinger, S. Lacoste-julien et al., Predicting solution summaries to integer linear programs under imperfect information with machine learning, 2018.

A. Lodi, G. ;. Zarpellon, A. Jourdan, P. Siarry, and R. Chelouah, A sensitivity analysis method aimed at enhancing the metaheuristics for continuous optimization, Artificial Intelligence Review, pp.1-23, 2017.

L. Mossina and E. Rachelson, Naive bayes classification for subset selection, 2017.

N. P. Padhy, Unit commitment-a bibliographical survey, IEEE Transactions on power systems, vol.19, issue.2, pp.1196-1205, 2004.

E. Rachelson, A. B. Abbes, and S. Diemer, Combining mixed integer programming and supervised learning for fast re-planning, 2010 22nd IEEE International Conference on Tools with Artificial Intelligence, vol.2, pp.63-70, 2010.

J. Read, B. Pfahringer, G. Holmes, and E. Frank, Classifier chains for multi-label classification. Machine learning, vol.85, pp.333-359, 2011.

A. Saltelli, M. Ratto, T. Andres, F. Campolongo, J. Cariboni et al., Global sensitivity analysis: the primer, 2008.

A. Schrijver, Theory of linear and integer programming, 1998.

G. Tsoumakas and I. Vlahavas, Random klabelsets: an ensemble method for multilabel classification, European Conference on Machine Learning, pp.406-417, 2007.

F. Vanderbeck and L. A. Wolsey, Reformulation and Decomposition of Integer Programs, p.431, 2010.
URL : https://hal.archives-ouvertes.fr/inria-00392254

H. P. Williams, Model building in mathematical programming, 2013.

M. L. Zhang and Z. H. Zhou, A review on multi-label learning algorithms, IEEE Transactions on Knowledge and Data Engineering, vol.26, issue.8, pp.1819-1837, 2014.

D. Zupani?, Values suggestion in mixed integer programming by machine learning algorithm, Electronic Notes in discrete Mathematics, vol.1, pp.74-83, 1999.