An Asymptotic Test of Optimality Conditions in Multiresponse Simulation Optimization

References

• Bashyam S., Fu M. C. Optimization of (s, S) inventory systems with random lead times and a service level constraint. Management Sci. (1998) 44(12, Part 2):243–256Link, Google Scholar
• Bettonvil B., del Castillo E., Kleijnen J. P. C. Statistical testing of optimality conditions in multiresponse simulation-based optimization. Eur. J. Oper. Res. (2009) 199(2):448–458Crossref, Google Scholar
• Efron B., Tibshirani R. J.An Introduction to the Bootstrapped (1993) (Chapman & Hall, New York) Crossref, Google Scholar
• Fu M. C. Optimization for simulation: Theory vs. practice. INFORMS J. Comput. (2002) 14(3):192–215Link, Google Scholar
• Gill P. E., Murray W., Wright M. H.Practical Optimization (2000) 12th ed.(Academic Press, London) Google Scholar
• Glasserman P.Monte Carlo Methods in Financial Engineering (2003) (Springer, New York) Crossref, Google Scholar
• Higle J. L., Sen S. Statistical verification of optimality conditions for stochastic programs with recourse. Ann. Oper. Res. (1991) 30(1–4):215–240Crossref, Google Scholar
• Karaesmen I., van Ryzin G. Overbooking with substitutable inventory classes. Oper. Res. (2004) 52(1):83–104Link, Google Scholar
• Khuri A. I., Ghosh S., Rao C. R. Multiresponse surface methodology. Handbook of Statistics, Design and Analysis of Experiments (1996) 13(Elsevier, Amsterdam) 377–406Crossref, Google Scholar
• Kleijnen J. P. C.Design and Analysis of Simulation Experiments (2008) (Springer Science + Business Media, New York) . [Chinese translation published by Publishing House of Electronics Industry, Beijing, 2010.]Google Scholar
• Kleijnen J. P. C., Wan J. Optimization of simulated systems: OptQuest and alternatives. Simulation Modelling Practice Theory (2007) 15(3):354–362Crossref, Google Scholar
• Kodde D. A., Palm F. C. Wald criteria for jointly testing equality and inequality restrictions. Econometrica (1986) 54(5):1243–1248Crossref, Google Scholar
• Lehmann E. L.Elements of Large-Sample Theory (1999) (Springer, New York) Crossref, Google Scholar
• Rao C. R., Lecam L. M., Neyman J. Least squares theory using an estimated dispersion matrix and its application to measurement of signals. Proc. Fifth Berkeley Sympos. Math. Statist. Probab. (1967) I(University of California Press, Berkeley) 355–372Google Scholar
• Rubinstein R. Y., Shapiro A.Discrete Event Systems: Sensitivity Analysis and Stochastic Optimization by the Score Function Method (1993) (John Wiley & Sons, Chichester, UK) Google Scholar
• Safizadeh M. H. Minimizing the bias and variance of the gradient estimate in RSM simulation studies. Eur. J. Oper. Res. (2002) 136(1):121–135Crossref, Google Scholar
• Shapiro A. Towards a unified theory of inequality constrained testing in multivariate analysis. Internat. Statist. Rev. (1988) 56(1):49–62Crossref, Google Scholar
• Shapiro A., Homem-de-Mello T. A simulation-based approach to two-stage stochastic programming with recourse. Math. Programming (1998) 81(3):301–325Crossref, Google Scholar
• Spall J. C.Introduction to Stochastic Search and Optimization: Estimation, Simulation, and Control (2003) (John Wiley & Sons, Hoboken, NJ) Crossref, Google Scholar
• Theil H.Principles of Econometrics (1971) (John Wiley & Sons, New York) Google Scholar
• Zazanis M. A., Suri R. Convergence rates of finite-difference sensitivity estimates for stochastic systems. Oper. Res. (1993) 41(4):694–703Link, Google Scholar