Stochastic Trust-Region Response-Surface Method (STRONG)—A New Response-Surface Framework for Simulation Optimization

Published Online:https://doi.org/10.1287/ijoc.1120.0498

References

  • Andradóttir S, Banks J. Simulation optimization. Handbook of Simulation: Principles, Methodology, Advances, Applications, and Practice (2007) (John Wiley & Sons, Hoboken, NJ) Google Scholar
  • Andrews DWK. Generic uniform convergence. Econometric Theory (1992) 8:241–257CrossrefGoogle Scholar
  • Angün E, Kleijnen J, Hertog DD, Gurkan G. Response surface methodology with stochastic constraints for expensive simulation. J. Oper. Res. Soc. (2009) 60(6):735–746CrossrefGoogle Scholar
  • Banks J. Handbook of Simulation: Principles, Methodology, Advances, Applications, and Practice (1998) (John Wiley & Sons, Hoboken, NJ) CrossrefGoogle Scholar
  • Barton RR, Ivey JS. Nelder-Mead simplex modifications for simulation optimization. Management Sci. (1996) 42(7):954–973LinkGoogle Scholar
  • Barton RR, Meckesheimer M, Henderson SG, Nelson BL. Metamodel-based simulation optimization. Handbooks in Operations Research and Management Science (2006) 13(Elsevier, Amsterdam) 535–574Google Scholar
  • Bartz-Beielstein T, Chiarandini M, Paquete L, Preuss M. Experimental Methods for the Analysis of Optimization Algorithms (2010) (Springer-Verlag, Berlin, Heidelberg) CrossrefGoogle Scholar
  • Bastin F, Cirillo C, Toint PL. An adaptive Monte Carlo algorithm for computing mixed logit estimators. Comput. Management Sci. (2006) 3:55–79CrossrefGoogle Scholar
  • Biles WE, Swain JJ. Optimization and Industrial Experimentation (1979) (Wiley-Interscience, New York) Google Scholar
  • Box GEP, Wilson KB. On the experimental attainment of optimum conditions. J. Royal Statist. Soc. (1951) 13:1–38Google Scholar
  • Cao XR. Realization Probabilities: The Dynamics of Queuing Systems (1994) (Springer-Verlag, New York) CrossrefGoogle Scholar
  • Chang K-H. Stochastic trust-region response-surface method (STRONG)—A new response-surface-based algorithm in simulation optimization. (2008) . Ph.D. thesis, Purdue University, West Lafayette, INGoogle Scholar
  • Chang K-H, Hong LJ, Wan H, Henderson SG, Biller B, Hsieh M-H, Shortle J, Tew JD, Barton RR. Stochastic trust region gradient-free method (STRONG)—A new response-surface-based algorithm in simulation optimization. Proc. 2007 Winter Simulation Conf. (2007) (IEEE, Piscataway, NJ) 346–354CrossrefGoogle Scholar
  • Conn AR, Gould NLM, Toint PL. Trust-Region Methods (2000) (SIAM, Philadelphia) CrossrefGoogle Scholar
  • Deng G, Ferris MC. Variable-number sample-path optimization. Math. Programming (2009) 117:81–109CrossrefGoogle Scholar
  • Fu MC. Optimization for simulation: Theory vs. practice. INFORMS J. Comput. (2002) 14:192–215LinkGoogle Scholar
  • Fu MC, Henderson SG, Nelson BL. Gradient estimation. Handbooks in Operations Research and Management Science (2006) 13(Elsevier, Amsterdam) 575–616Google Scholar
  • Glynn PW. Likelihood ratio gradient estimation for stochastic systems. Comm. ACM (1990) 35(10):75–84CrossrefGoogle Scholar
  • Ho YC, Cao XR. Discrete Event Dynamic Systems and Perturbation Analysis (1991) (Kluwer Academic Publishers, Boston) CrossrefGoogle Scholar
  • Hong LJ. Estimating quantile sensitivities. Oper. Res. (2009) 57(1):118–130LinkGoogle Scholar
  • Hood SJ, Welch PD, Evans GW, Mollaghasemi M, Russel EC, Biles WE. Response surface methodology and its applications in simulation. Proc. 1993 Winter Simulation Conf. (1993) (IEEE, Piscataway, NJ) 115–122CrossrefGoogle Scholar
  • Khuri AI, Cornell JA. Response Surfaces Designs and Analyses (1996) (Marcel Dekker, New York) Google Scholar
  • Kiefer J, Wolfowitz J. Stochastic estimation of the maximum of a regression function. Ann. Math. Statist. (1952) 23(3):462–466CrossrefGoogle Scholar
  • Kleijnen JPC, Banks J. Experimental design for sensitivity analysis, optimization, and validation of simulation models. Handbook of Simulation: Principles, Methodology, Advances, Applications, and Practice (1998) (John Wiley & Sons, New York) CrossrefGoogle Scholar
  • Kleijnen JPC. Design and Analysis of Simulation Experiments (2008) (Springer, New York) Google Scholar
  • Kleijnen JPC, Hertog DD, Angün E. Response surface methodology's steepest ascent and step size revisited. Eur. J. Oper. Res. (2004) 159(1):121–131CrossrefGoogle Scholar
  • Kleijnen JPC, Sanches SM, Lucas TW, Cioppa TM. A user's guide to the brave new world of designing simulation experiments. INFORMS J. Comput. (2005) 17(3):263–289LinkGoogle Scholar
  • Kushner HJ, Yin GG. Stochastic Approximation Algorithms and Applications (1997) (Springer-Verlag, New York) CrossrefGoogle Scholar
  • Lai TL, Robbins H, Wei CZ. Strong consistency of least squares estimates in multiple regression. J. Multivariate Anal. (1979) 9:343–362CrossrefGoogle Scholar
  • Law AM. Simulation Modeling and Analysis (2007) 4th ed.(McGraw-Hill, Boston) Google Scholar
  • Ljung L, Pflug G, Walk H. Stochastic Approximation and Optimization of Random Systems (1992) (Birkhäuser, Basel, Berlin) CrossrefGoogle Scholar
  • Montgomery DC. Design and Analysis of Experiments (2005) 6th ed.(John Wiley & Sons, New York) Google Scholar
  • More JJ, Garbow BS, Hillstrom KE. Testing unconstrained optimization software. ACM Trans. Math. Software (1981) 7(1):17–41CrossrefGoogle Scholar
  • Myers RH, Montgomery DC, Anderson-Cook CM. Response Surface Methodology-Process and Product Optimization Using Designed Experiments (2009) (John Wiley & Sons, New York) Google Scholar
  • Neddermeijer HG, Oortmarssen GJV, Piersma N, Dekker R, Joines JA, Barton RR, Kang K, Fishwick PA. A framework for response surface methodology for simulation optimization. Proc. 2000 Winter Simulation Conf. (2000) (IEEE, Piscataway, NJ) 129–136CrossrefGoogle Scholar
  • Nicolai RP, Dekker R, Piersma N, Oortmarssen GJV, Ingalls RG, Rossetti MD, Smith JS, Peters BA. Automated response surface methodology for stochastic optimization models with unknown variance. Proc. 2004 Winter Simulation Conf. (2004) 491–499CrossrefGoogle Scholar
  • Nocedal J, Wright SJ. Numerical Optimization (1999) (Springer, New York) CrossrefGoogle Scholar
  • Rubinstein RY, Shapiro A. Discrete Event Systems: Sensitivity Analysis and Stochastic Approximation Using the Score Function Method (1993) (John Wiley & Sons, New York) Google Scholar
  • Sakalauskas L, Krarup J. Editorial; heuristic and stochastic methods in optimization. Eur. J. Oper. Res. (2006) 171(3):723–724CrossrefGoogle Scholar
  • Sanchez SM, Mason SJ, Hill RR, Monch L, Rose O, Jefferson T, Fowler JW. Better than a petaflop: The power of efficient experimental design. Proc. 2008 Winter Simulation Conf. (2008) (IEEE, Piscataway, NJ) 73–84CrossrefGoogle Scholar
  • Serfling RJ. Approximation Theorems of Mathematical Statistics (1980) (John Wiley & Sons, New York) CrossrefGoogle Scholar
  • Shang Y-W, Qiu Y-H. A note on the extended Rosenbrock function. Evolutionary Comput. (2006) 14(1):119–126CrossrefGoogle Scholar
  • Spall JC. Adaptive stochastic approximation by the simultaneous pertubation method. IEEE Trans. Automatic Control (2000) 45(10):1839–1853CrossrefGoogle Scholar
  • Spall JC. Introduction to Stochastic Search and Optimization: Estimation, Simulation, and Control (2003) (John Wiley & Sons, New York) CrossrefGoogle Scholar
  • Tekin E, Sabuncuoglu I. Simulation optimization: A comprehensive review on theory and applications. IIE Trans. (2004) 36(11):1067–1081CrossrefGoogle Scholar
  • Wu CFJ, Hamada M. Experiments: Planning, Analysis, and Parameter Design Optimization (2000) (John Wiley & Sons, New York) Google Scholar
  • Yin G, Zhu Y. Averaging procedures in adaptive filtering: An efficient approach. IEEE Trans. Automatic Control (1992) 37(4):466–475CrossrefGoogle Scholar
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