Chance Constrained Selection of the Best

References

• Andradóttir S, Kim S-H (2010) Fully sequential procedures for comparing constrained systems via simulation. Naval Res. Logist. 57:403–421.Crossref, Google Scholar
• Batur D, Kim S-H (2010) Finding feasible systems in the presence of constraints on multiple performance measures. ACM Trans. Modeling Comput. Simulation 20:1–26.Crossref, Google Scholar
• Bechhofer RE (1954) A single-sample multiple decision procedure for ranking means of normal populations with known variances. Ann. Math. Statist. 25:16–39.Crossref, Google Scholar
• Bechhofer RE, Santner TJ, Goldsman DM (1995) Design and Analysis of Experiments for Statistical Selection, Screening, and Multiple Comparisons (John Wiley & Sons, New York).Google Scholar
• Birge JR, Louveaux F (1997) Introduction to Stochastic Programming, Springer Series in Operations Research Series (Springer, London).Google Scholar
• Butler J, Morrice DJ, Mullarkey PW (2001) A multiple attribute utility theory approach to ranking and selection. Management Sci. 47:800–816.Link, Google Scholar
• Casella G, Berger RL (2002) Statistical Inference, 2nd ed. (Duxbury Thomson Learning, Pacific Grove, CA).Google Scholar
• Charnes A, Cooper WW, Symonds GH (1958) Cost horizons and certainty equivalents: An approach to stochastic programming of heating oil. Management Sci. 4:235–263.Link, Google Scholar
• Chen C-H (1996) A lower bound for the correct subset-selection probability and its application to discrete event simulations. IEEE Trans. Automatic Control 41:1227–1231.Crossref, Google Scholar
• Chen C-H, Lin J, Yücesan E, Chick SE (2000) Simulation budget allocation for further enhancing the efficiency of ordinal optimization. Discrete Event Dynamic Systems 10:251–270.Crossref, Google Scholar
• Frazier PI (2010) Decision-theoretic foundations of simulation optimization. Cochran JJ, Cox LA, Keskinocak P, Kharoufeh JP, Smith JC, eds. Wiley Encyclopedia of Operations Research and Management Science (John Wiley & Sons, New York), 1–10.Google Scholar
• Healey C, Andradóttir S, Kim S-H (2013) Efficient comparison of constrained systems using dormancy. Eur. J. Oper. Res. 224:340–352.Crossref, Google Scholar
• Hong LJ, Yang Y, Zhang L (2011) Sequential convex approximations to joint chance constrained programs: A Monte Carlo approach. Oper. Res. 59:617–630.Link, Google Scholar
• Hunter SR, Pasupathy R (2013) Optimal sampling laws for stochastically constrained simulation optimization on finite sets. INFORMS J. Comput. 25:527–542.Link, Google Scholar
• Kim S-H (2005) Comparison with a standard via fully sequential procedures. ACM Trans. Modeling Comput. Simulation 15:155–174.Crossref, Google Scholar
• Kim S-H, Nelson BL (2001) A fully sequential procedure for indifference-zone selection in simulation. ACM Trans. Modeling Comput. Simulation 11:251–273.Crossref, Google Scholar
• Kim S-H, Nelson BL (2006) Selecting the best system. Henderson SG, Nelson BL, eds. Elsevier Handbooks in Operations Research and Management Science: Simulation (Elsevier, Waltham, MA), 501–534.Google Scholar
• Lee LH, Chew EP, Teng S, Goldsman D (2010) Finding the non-dominated pareto set for multi-objective simulation models. IIE Trans. 42:656–674.Crossref, Google Scholar
• Lee LH, Pujowidianto NA, Li L-W, Chen C-H, Yap CM (2012) Approximate simulation budget allocation for selecting the best design in the presence of stochastic constraints. IEEE Trans. Automatic Control 57:2940–2945.Crossref, Google Scholar
• Miller BL, Wagner HM (1965) Chance constrained programming with joint constraints. Oper. Res. 13:930–945.Link, Google Scholar
• Nelson BL (2013) Foundations and Methods of Stochastic Simulation: A First Course (Springer, New York).Crossref, Google Scholar
• Nelson BL, Goldsman D (2001) Comparisons with a standard in simulation experiments. Management Sci. 47:449–463.Link, Google Scholar
• Pasupathy R, Hunter SR, Pujowidianto NA, Lee LH, Chen CH (2014) Stochastically constrained ranking and selection via SCORE. ACM Trans. Modeling Comput. Simulation 25:1–26.Crossref, Google Scholar
• Prékopa A (2003) Probabilistic programming. Ruszczynski A, Shapiro A, eds. Elsevier Handbooks in Operations Research and Management Science: Stochastic Programming (Elsevier, Waltham, MA), 267–351.Crossref, Google Scholar
• Siegmund D (1985) Sequential Analysis: Tests and Confidence Intervals (Springer, New York).Crossref, Google Scholar
• Tamhane AC, Dunlop DD (1999) Statistics and Data Analysis: From Elementary to Intermediate (Prentice-Hall, Upper Saddle River, NJ).Google Scholar
• Xu J, Nelson BL, Hong LJ (2010) Industrial strength COMPASS: A comprehensive algorithm and software for optinization via simulation. ACM Trans. Modeling Comput. Simulation 20:1–29.Crossref, Google Scholar