Some Monotonicity Results for Stochastic Kriging Metamodels in Sequential Settings

Published Online:https://doi.org/10.1287/ijoc.2017.0779

References

  • Abramowitz M, Stegun IA (1972) Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables (National Bureau of Standards, Gaithersburg, MD).Google Scholar
  • Ajdari A, Mahlooji H (2014) An adaptive exploration-exploitation algorithm for constructing metamodels in random simulation using a novel sequential experimental design. Comm. Statist.—Simulation Comput. 43(5):947–968.CrossrefGoogle Scholar
  • Ankenman B, Nelson BL, Staum J (2010) Stochastic kriging for simulation metamodeling. Oper. Res. 58(2):371–382.LinkGoogle Scholar
  • Barton RR (2009) Simulation optimization using metamodels. Proc. 2009 Winter Simulation Conf. (IEEE, Piscataway, NJ), 230–238.Google Scholar
  • Barton RR, Meckesheimer M (2006) Metamodel-based simulation optimization. Henderson SG, Nelson BL, eds. Handbooks Oper. Res. Management Sci. Vol. 13 (North-Holland, Amsterdam),535–574.CrossrefGoogle Scholar
  • Chang KH, Hong LJ, Wan H (2013) Stochastic trust-region response-surface method (STRONG)—A new response-surface framework for simulation optimization. INFORMS J. Comput. 25(2):230–243.LinkGoogle Scholar
  • Chen X, Ankenman BE, Nelson BL (2013a) Enhancing stochastic kriging metamodels with gradient estimators. Oper. Res. 61(2):512–528.LinkGoogle Scholar
  • Chen X, Wang K, Yang F (2013b) Stochastic kriging with qualitative factors. Proc. 2013 Winter Simulation Conf. (IEEE, Piscataway, NJ), 790–801.Google Scholar
  • den Hertog D, Kleijnen J, Siem A (2006) The correct kriging variance estimated by bootstrapping. J. Oper. Res. Soc. 57(4):400–409.CrossrefGoogle Scholar
  • Hu J, Hu P (2011) Annealing adaptive search, cross-entropy, and stochastic approximation in global optimization. Naval Res. Logist. (NRL) 58(5):457–477.CrossrefGoogle Scholar
  • Jin R, Chen W, Sudjianto A (2002) On sequential sampling for global metamodeling in engineering design. ASME 2002 Internat. Design Engrg. Tech. Conf. Comput. Inform. Engrg. Conf. (ASME, New York), 539–548.Google Scholar
  • Kleijnen JP (2007) Design and Analysis of Simulation Experiments, Vol. 111 (Springer International, Cham, Switzerland).Google Scholar
  • Kleijnen JP (2009) Kriging metamodeling in simulation: A review. Eur. J. Oper. Res. 192(3):707–716.CrossrefGoogle Scholar
  • Loeppky JL, Sacks J, Welch WJ (2009) Choosing the sample size of a computer experiment: A practical guide. Technometrics 51(4):366–376.CrossrefGoogle Scholar
  • Qu H, Fu MC (2014) Gradient extrapolated stochastic kriging. ACM Trans. Modeling Comput. Simulation 24(4):Article No. 23.CrossrefGoogle Scholar
  • Ranjan P, Haynes R, Karsten R (2011) A computationally stable approach to gaussian process interpolation of deterministic computer simulation data. Technometrics 53(4):366–378.CrossrefGoogle Scholar
  • Sacks J, Welch WJ, Mitchell TJ, Wynn HP (1989) Design and analysis of computer experiments. Statist. Sci. 4(4):409–423.CrossrefGoogle Scholar
  • Santner TJ, Williams BJ, Notz W (2003) The Design and Analysis of Computer Experiments (Springer, New York).CrossrefGoogle Scholar
  • Ulaganathan S, Couckuyt I, Dhaene T, Laermans E, Degroote J (2014) On the use of gradients in kriging surrogate models. Proc. 2014 Winter Simulation Conf. (IEEE, Piscataway, NJ), 2692–2701.Google Scholar
  • Vazquez E, Bect J (2010) Pointwise consistency of the kriging predictor with known mean and covariance functions. mODa 9—Advances in Model-Oriented Design and Analysis. Contributions to Statistics (Physica-Verlag, Heidelberg, Germany), 221–228.CrossrefGoogle Scholar
  • Viana FA, Simpson TW, Balabanov V, Toropov V (2014) Metamodeling in multidisciplinary design optimization: How far have we really come? AIAA J. 52(4):670–690.CrossrefGoogle Scholar
  • Wang GG, Shan S (2006) Review of metamodeling techniques in support of engineering design optimization. J. Mech. Design 129(4):370–380.CrossrefGoogle Scholar
  • Wang L (2005) A hybrid genetic algorithm-neural network strategy for simulation optimization. Appl. Math. Comput. 170(2):1329–1243.Google Scholar
  • Xie W, Nelson B, Staum J (2010) The influence of correlation functions on stochastic kriging metamodels. Proc. 2010 Winter Simulation Conf. (IEEE, Piscataway, NJ), 1067–1078.Google Scholar
  • Xie W, Nelson BL, Barton RR (2014) A bayesian framework for quantifying uncertainty in stochastic simulation. Oper. Res. 62(6):1439–1452.LinkGoogle Scholar
  • Yang F, Liu J, Nelson BL, Ankenman BE, Tongarlak M (2011) Metamodelling for cycle time-throughput-product mix surfaces using progressive model fitting. Production Planning and Control 22(1):50–68.CrossrefGoogle Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.