Dynamic Sampling Allocation and Design Selection

References

• Bechhofer RE, Santner TJ, Goldsman DM (1995) Design and Analysis for Statistical Selection, Screening, and Multiple Comparisons (John Wiley & Sons, New York).Google Scholar
• Berger JO, Deely J (1988) A Bayesian approach to ranking and selection of related means with alternatives to analysis-of-variance methodology. J. Amer. Statist. Assoc. 83(402):364–373.Crossref, Google Scholar
• Branke J, Chick SE, Schmidt C (2007) Selecting a selection procedure. Management Sci. 53(12):1916–1932.Link, Google Scholar
• Chen CH, He D (2005) Intelligent simulation for alternatives comparison and application to air traffic management. J. Systems Sci. Systems Engrg. 14(1):37–51.Crossref, Google Scholar
• Chen CH, Lee LH (2011) Stochastic Simulation Optimization: An Optimal Computing Budget Allocation (World Scientific Publishing Company, River Edge, NJ).Google Scholar
• Chen CH, He D, Fu MC (2006) Efficient dynamic simulation allocation in ordinal optimization. IEEE Trans. Automatic Control 51(12): 2005–2009.Crossref, Google Scholar
• Chen CH, Donohue K, Yücesan E, Lin J (2003) Optimal computing budget allocation for Monte Carlo simulation with application to product design. Simulation Modelling Practice Theory 11(1):57–74.Crossref, Google Scholar
• Chen CH, Lin J, Yücesan E, Chick SE (2000) Simulation budget allocation for further enhancing the efficiency of ordinal optimization. Discrete Event Dynamic Systems 10(3):251–270.Crossref, Google Scholar
• Chick SE, Gans N (2009) Economic analysis of simulation selection problems. Management Sci. 55(3):421–437.Link, 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
• Chick SE, Branke J, Schmidt C (2010) Sequential sampling to myopically maximize the expected value of information. INFORMS J. Comput. 22(1):71–80.Link, Google Scholar
• DeGroot MH (2005) Optimal Statistical Decisions (Wiley-Interscience, Hoboken, NJ).Google Scholar
• Frazier PI (2012) Tutorial: Optimization via simulation with Bayesian statistics and dynamic programming. Laroque C, Himmelspach J, Pasupathy R, Rose O, Uhrmacher AM, eds. Proc. 2012 Winter Simulation Conf. (IEEE, Piscataway, NJ), 1–16.Crossref, Google Scholar
• Frazier PI, Powell WB, Dayanik S (2008) A knowledge-gradient policy for sequential information collection. SIAM J. Control Optim. 47(5):2410–2439.Crossref, Google Scholar
• Fu MC, Hu J-Q, Chen C-H, Xiong X (2007) Simulation allocation for determining the best design in the presence of correlated sampling. INFORMS J. Comput. 19(1):101–111.Link, Google Scholar
• Glynn PW, Juneja S (2004) A large deviations perspective on ordinal optimization. Ingalls RG, Rossetti MD, Smith JS, Peter BA, eds. Proc. 2004 Winter Simulation Conf. (IEEE, Piscataway, NJ), 577–585.Crossref, Google Scholar
• Goldsman D, Nelson BL (1998) Comparing systems via simulation. Banks J, ed. Handbook of Simulation: Principles, Methodology, Advances, Applications, and Practice (John Wiley & Sons, New York), 273–306.Crossref, Google Scholar
• Gross D, Shortle JF, Thompson JM, Harris CM (2013) Fundamentals of Queueing Theory (John Wiley & Sons, Hoboken, NJ).Google Scholar
• Gupta SS, Miescke KJ (1996) Bayesian look ahead one-stage sampling allocations for selection of the best population. J. Statist. Planning Inference 54(2):229–244.Crossref, Google Scholar
• He D, Chick SE, Chen CH (2007) Opportunity cost and OCBA selection procedures in ordinal optimization for a fixed number of alternative systems. IEEE Trans. Systems, Man, Cybernetics, Part C: Appl. Rev. 37(5):951–961.Crossref, Google Scholar
• Inoue K, Chick SE (1998) Comparison of Bayesian and frequentist assessments of uncertainty for selecting the best system. Medeiros DJ, Watson EF, Carson JS, Manivannan MS, eds. Proc. 1998 Winter Simulation Conf. (IEEE Computer Society Press, Piscataway, NJ), 727–734.Crossref, Google Scholar
• Inoue K, Chick SE, Chen C-H (1999) An empirical evaluation of several methods to select the best system. ACM Trans. Modeling Comput. Simulation 9(4):381–407.Crossref, Google Scholar
• Jones GL (2004) On the Markov chain central limit theorem. Probab. Surveys 1:299–320.Crossref, Google Scholar
• Kim S-H, Nelson BL (2006) Selecting the best system. Henderson SG, Nelson BL, eds. Handbooks in Operations Research and Management Science: Simulation, Vol. 13 (Elsevier, Amsterdam), 501–534.Google Scholar
• Law AM, Kelton WD (2000) Simulation Modeling and Analysis (McGraw-Hill, Boston).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):1–26.Crossref, Google Scholar
• Peng Y, Chen C-H, Fu MC, Hu J-Q (2013a) A dynamic framework for statistical selection problems. Pasupathy R, Kim S-H, Tolk A, Hill R, Kuhl ME, eds. Proc. 2013 Winter Simulation Conf. (IEEE, Piscataway, NJ) 908–921.Crossref, Google Scholar
• Peng Y, Chen C-H, Fu MC, Hu J-Q (2013b) Efficient simulation resource sharing and allocation for selecting the best. IEEE Trans. Automatic Control 58(4):1017–1023.Crossref, Google Scholar
• Powell WB, Ryzhov IO (2012) Ranking and selection. Optimal Learning (John Wiley & Sons, Hoboken, NJ), 71–88.Crossref, Google Scholar
• Qu H, Ryzhov IO, Fu MC (2012) Ranking and selection with unknown correlation structures. Laroque C, Himmelspach J, Pasupathy R, Rose O, Uhrmacher AM, eds. Proc. 2012 Winter Simulation Conf. (IEEE, Piscataway, NJ), 1–12.Crossref, Google Scholar
• Ross A (2003) Useful bounds on the expected maximum of correlated normal random variables. Working Paper 03W-004, Technical report, Industrial and Systems Engineering Department, Lehigh University, Bethlehem, PA.Google Scholar