Methods for System Selection Based on Sequential Mean–Variance Analysis

References

• Andradóttir S, Kim SH (2010) Fully sequential procedures for comparing constrained systems via simulation. Naval Res. Logist. 57(5):403–421.Crossref, Google Scholar
• Banks J, Carson JS, Nelson BL, Nicol DM (2010) Discrete-Event System Simulation (Prentice Hall, Inc., Upper Saddle River, NJ), 476–477.Google Scholar
• Batur D, Choobineh F (2010a) Mean-variance based ranking and selection. Johansson B, Jain S, Montoya-Torres J, Hugan J, Yucesan E, eds. 2010 Winter Simulation Conf. Proc. (Baltimore, MD), 1160–1166.Google Scholar
• Batur D, Choobineh F (2010b) A quantile-based approach to system selection. Eur. J. Oper. Res. 202(3):764–772.Crossref, Google Scholar
• Batur D, Choobineh F (2012) Stochastic dominance based comparison for system selection. Eur. J. Oper. Res. 220(3):661–672.Crossref, Google Scholar
• Batur D, Kim SH (2010) Finding feasible systems in the presence of constraints on multiple performance measures. ACM Trans. Modeling Comput. Simulation 20(3):Article no. 13.Crossref, Google Scholar
• Bekki JM, Fowler JW, Mackulak GT, Nelson BL (2007) Using quantiles in ranking and selection procedures. Henderson SG, Biller B, Hsieh M-H, Shortle J, Tew JD, Barton RR, eds. 2007 Winter Simulation Conf. Proc., Washington, DC, 1722–1728.Crossref, Google Scholar
• Butler J, Morrice DJ, Mullarkey PW (2001) A multiple attribute utility theory approach to ranking and selection. Management Sci. 47(6):800–816.Link, Google Scholar
• Chen CH (1996) A lower bound for the correct subset-selection probability and its application to discrete event simulation. IEEE Trans. Automatic Control 41(8):1227–1231.Crossref, Google Scholar
• Chen HC, Chen CH, Yücesan E (2000) Computing efforts allocation for ordinal optimization and discrete event simulation. IEEE Trans. Automatic Control 45(5):960–964.Crossref, Google Scholar
• Chick SE (2006) Subjective probability and Bayesian methodology. Henderson SG, Nelson BL, eds. Handbooks in Operations Research and Management Science: Simulation (Elsevier, Oxford, UK),225–257.Google Scholar
• Chick SE, Inoue K (2001) New two-stage and sequential procedures for selecting the best simulated system. Oper. Res. 49(5):732–743.Link, Google Scholar
• Goldsman D, Kim S-H, Marshall WS, Nelson BL (2002) Ranking and selection for steady-state simulation: Procedures and perspectives. INFORMS J. Comput. 14(1):2–19.Link, Google Scholar
• Hong LJ, Nelson BL (2005) The tradeoff between sampling and switching: New sequential procedures for indifference-zone selection. IIE Trans. 37(7):623–634.Crossref, Google Scholar
• Hunter S, Pasupathy R (2013) Optimal sampling laws for stochastically constrained simulation optimization on finite sets. INFORMS J. Comput. 25(3):527–542.Link, Google Scholar
• Kabirian A, Olafsson S (2009) Selection of the best with stochastic constraints. Rossetti MD, Hill RR, Johansson B, Dunkin A, Ingalls RG, eds. 2009 Winter Simulation Conf. Proc., Austin, TX, 574–583.Google Scholar
• Kim SH (2005) Comparison with a standard via fully sequential procedures. ACM Trans. Modeling Comput. Simulation 15(2):155–174.Crossref, Google Scholar
• Kim SH, Nelson BL (2001) Fully sequential procedure for indifference-zone selection in simulation. ACM Trans. Modeling Comput. Simulation 11(3):251–273.Crossref, Google Scholar
• Kim SH, Nelson BL (2006) On the asymptotic validity of fully sequential selection procedures for steady-state simulation. Oper. Res. 54(3):475–488.Link, 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(9):656–674.Crossref, Google Scholar
• Lee LH, Pujowidianto NA, Li LW, Chen CH, Yap CM (2012) Approximate simulation budget allocation for selecting the best design in the presence of stochastic constraints. IEEE Trans. Automatic Control 57(11):2940–2945.Crossref, Google Scholar
• Levy H (1998) Stochastic Dominance: Investment Decision Making Under Uncertainty (Kluwer Academic Publishers, Boston).Crossref, Google Scholar
• Markowitz H (1952) Portfolio selection. J. Finance 7(1):77–91.Google Scholar
• Montgomery DC, Runger GC (2011) Applied Statistics and Probability for Engineers (John Wiley & Sons, Inc., Hoboken, NJ),286–288.Google Scholar
• Nakayama MK (2009) A general framework for the asymptotic validity of two-stage procedures for selection and multiple comparisons with consistent variance estimators. Rossetti MD, Hill RR, Johansson B, Dunkin A, Ingalls RG, eds. 2009 Winter Simulation Conf. Proc., Austin, TX, 716–722.Google Scholar
• Nelson BL, Goldsman D (2001) Comparisons with a standard in simulation experiments. Management Sci. 47(3):449–463.Link, Google Scholar
• Paulson E (1964) Sequential estimation and closed sequential decision procedures. Ann. Math. Statist. 35(3):1048–1058.Crossref, Google Scholar
• Rinott Y (1978) On two-stage procedures and related probability-inequalities. Comm. Statist. A7(8):799–811.Crossref, Google Scholar
• Santner TJ, Tamhane AC (1984) Designing experiments for selecting a normal population with a large mean and a small variance. Santner TJ, Tamhane AC, eds. Design of Experiments: Ranking and Selection (Marcel Dekker, New York), 179–198.Google Scholar