J. Sacks, W. J. Welch, T. J. Mitchell, and H. P. Wynn, Design and analysis of computer experiments, Statist. Sci, vol.4, issue.4, pp.409-423, 1989.

D. R. Jones, A taxonomy of global optimization methods based on response surfaces, Journal of Global Optimization, vol.21, issue.4, pp.345-383, 2001.

G. Wang and S. Shan, Review of metamodeling techniques in support of engineering design optimization, Journal of Mechanical Design, vol.129, 2007.

A. I. Forrester and A. J. Keane, Recent advances in surrogate-based optimization, Progress in Aerospace Sciences, vol.45, issue.1, pp.50-79, 2009.

D. Jones, M. Schonlau, and W. Welch, Efficient global optimization of expensive black-box functions, Journal of Global Optimization, vol.13, pp.455-492, 1998.

R. Morgans, A. C. Zander, C. Hansen, and D. Murphy, Ego shape optimization of horn-loaded loudspeakers, Optimization and Engineering, vol.9, pp.361-374, 2008.

M. Zaefferer, J. Stork, M. Friese, A. Fischbach, B. Naujoks et al., Efficient global optimization for combinatorial problems, GECCO 2014 -Proceedings of the 2014 Genetic and Evolutionary Computation Conference, 2014.

C. Sabater and S. Görtz, An efficient bi-level surrogate approach for optimizing shock control bumps under uncertainty, 2019.

M. M. Islam, H. K. Singh, and T. Ray, A surrogate assisted approach for single-objective bilevel optimization, IEEE Transactions on Evolutionary Computation, vol.21, issue.5, pp.681-696, 2017.

J. Toubeau, Z. De-grève, and F. Vallée, Medium-term multi-market optimization for virtual power plants: a stochastic based decision environment, IEEE Transactions on Power Systems, vol.33, issue.2, pp.1399-1410, 2018.

J. Toubeau, J. Bottieau, F. Vallée, and Z. De-grève, Deep learningbased multivariate probabilistic forecasting for short-term scheduling in power markets, IEEE Transactions on Power Systems, vol.34, issue.2, pp.1203-1215, 2019.

D. Ginsbourger, R. Le-riche, and L. Carraro, A Multi-points Criterion for Deterministic Parallel Global Optimization based on Gaussian Processes, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00260579

D. Gorissen, K. Crombecq, I. Couckuyt, T. Dhaene, and P. Demeester, A surrogate modeling and adaptive sampling toolbox for computer based design, Journal of Machine Learning Research, vol.11, pp.2051-2055, 2010.

, Matlab optimization toolbox, the MathWorks

O. Roustant, D. Ginsbourger, and Y. Deville, Dicekriging, diceoptim: Two r packages for the analysis of computer experiments by krigingbased metamodeling and optimization, Journal of Statistical Software, Articles, vol.51, issue.1, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00495766

K. Bruninx, Improved modeling of unit commitment decisions under uncertainty, 2016.

J. Toubeau, F. Vallée, Z. De-grève, and J. Lobry, A new approach based on the experimental design method for the improvement of the operational efficiency in medium voltage distribution networks, International Journal of Electrical Power and Energy Systems, vol.66, pp.116-124, 2015.

H. Pand?i?, I. Kuzle, and T. Capuder, Virtual power plant mid-term dispatch optimization, Applied Energy, vol.101, p.2013

G. Matheron, Principles of geostatistics, Economic Geology, vol.58, pp.1246-1266, 1963.

N. Cressie, Statistics for Spatial Data, 1993.

M. Schonlau, Computer experiments and global optimization, 1997.

A. Sóbester, S. J. Leary, and A. Keane, On the design of optimization strategies based on global response surface approximation models, Journal of Global Optimization, vol.33, pp.31-59, 2005.

B. Henrik, J. Andy, and R. Jason, Read and write mat files and call matlab from within r

D. Dupuy, C. Helbert, and J. Franco, DiceDesign and DiceEval: Two R packages for design and analysis of computer experiments, Journal of Statistical Software, vol.65, issue.11, pp.1-38, 2015.
URL : https://hal.archives-ouvertes.fr/hal-02065877