Knockout-Tournament Procedures for Large-Scale Ranking and Selection in Parallel Computing Environments

References

• Bechhofer RE (1954) A single-sample multiple decision procedure for ranking means of normal populations with known variances. Ann. Math. Statist. 25(1):16–39.Crossref, Google Scholar
• Bechhofer RE, Goldsman DM, Santner TJ (1995) Design and Analysis of Experiment for Statistical Selection, Screening, and Multiple Comparisons, 1st ed. (Wiley, New York).Google Scholar
• Buzacott JA, Shanthikumar JG (1993) Stochastic Models of Manufacturing Systems, vol. 4, 1st ed. (Pearson, Upper Saddle River, NJ).Google Scholar
• Chen CH, Lin J, Yücesan E, Chick SE (2000) Simulation budget allocation for further enhancing the efficiency of ordinal optimization. Discrete Event Dynam. Systems 10(3):251–270.Crossref, Google Scholar
• Chick SE (2006) Subjective probability and Bayesian methodology. Henderson SG, Nelson BL, eds. Handbook in Operations Research and Management Science: Simulation, vol. 13 (Elsevier, Amsterdam), 225–257.Google Scholar
• Chick SE, Frazier P (2012) Sequential sampling with economics of selection procedures. Management Sci. 58(3):550–569.Link, 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
• Cormen TH, Leiserson CE, Rivest RL, Stein C (2001) Introduction to Algorithms, 2nd ed. (The MIT Press, Cambridge, MA).Google Scholar
• Dudewicz EJ, Dalal SR (1975) Allocation of observations in ranking and selection with unequal variances. Sankhya Indian J. Statist. Ser. B 37(1):28–78.Google Scholar
• Even-Dar E, Mannor S, Mansour Y (2002) PAC bounds for multi-armed bandit and Markov decision processes. Kivinen J, Sloan R, eds. Proc. 15th Internat. Conf. Computational Learning Theory (Springer, Berlin), 255–270.Google Scholar
• Fan W, Hong LJ, Nelson BL (2016) Indifference-zone-free selection of the best. Oper. Res. 64(5):1499–1514.Link, Google Scholar
• Frazier PI (2014) A fully sequential elimination procedure for indifference-zone ranking and selection with tight bounds on probability of correct selection. Oper. Res. 62(4):926–942.Link, 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
• Frazier PI, Powell WB, Dayanik S (2009) The knowledge-gradient policy for correlated normal beliefs. INFORMS J. Comput. 21(4):599–613.Link, Google Scholar
• Hong LJ (2006) Fully sequential indifference-zone selection procedures with variance-dependent sampling. Naval Res. Logist. 53(5):464–476.Crossref, Google Scholar
• Hunter SR, Nelson BL (2017) Parallel ranking and selection. Tolk A, Fowler J, Shao G, Yücesan E, eds. Advances in Modeling and Simulation: Seminal Research from 50 Years of Winter Simulation Conferences (Springer, Cham, Switzerland), 249–275.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, Piscataway, NJ), 727–734.Google Scholar
• Jamieson K, Malloy M, Nowak R, Bubeck S (2014) Lil’ UCB: An optimal exploration algorithm for multi-armed bandits. Balcan MF, Feldman V, Szepesvári C, eds. Proc. 27th Conf. Learn. Theory (PMLR, Barcelona, Spain), 423–439.Google Scholar
• Kao SC, Lai TL (1980) Sequential selection procedures based on confidence sequences for normal populations. Comm. Statist. Theory Methods 9(16):1657–1676.Crossref, Google Scholar
• Kim SH, Nelson BL (2001) A fully sequential procedure for indifference-zone selection in simulation. ACM Trans. Model. Comput. Simulation 11(3):251–273.Crossref, Google Scholar
• Kim S-H, Nelson BL (2006) Selecting the best system. Henderson SG, Nelson BL, eds. Handbook in Operations Research and Management Science: Simulation, vol. 13 (Elsevier, Amsterdam), 501–534.Google Scholar
• Kim MP, Suksompong W, Williams VV (2017) Who can win a single-elimination tournament? SIAM J. Discrete Math. 31(3):1751–1764.Crossref, Google Scholar
• Luo J, Hong LJ (2011) Large-scale ranking and selection using cloud computing. Jain S, Creasey R, Himmelspach J, White KP, Fu M, eds. Proc. 2011 Winter Simulation Conf. (IEEE, Piscataway, NJ), 4046–4056.Google Scholar
• Luo J, Hong LJ, Nelson BL, Wu Y (2015) Fully sequential procedures for large-scale ranking-and-selection problems in parallel computing environments. Oper. Res. 63(5):1177–1194.Link, Google Scholar
• Nelson BL, Matejcik FJ (1995) Using common random numbers for indifference-zone selection and multiple comparisons in simulation. Management Sci. 41(12):1935–1945.Google Scholar
• Ni EC, Henderson SG, Hunter SR (2014) A comparison of two parallel ranking and selection procedures. Tolk A, Diallo S, Ryzhov IO, Yilmaz L, eds. Proc. 2014 Winter Simulation Conf. (IEEE, Piscataway, NJ), 3761–3772.Google Scholar
• Ni EC, Ciocan DF, Henderson SG, Hunter SR (2017) Efficient ranking and selection in parallel computing environments. Oper. Res. 65(3):821–836.Link, Google Scholar
• Paulson E (1964) A sequential procedure for selecting the population with the largest mean from k normal populations. Ann. Math. Statist. 35(1):174–180.Crossref, Google Scholar
• Peng Y, Chong EK, Chen CH, Fu MC (2018) Ranking and selection as stochastic control. IEEE Trans. Automatic Control 63(8):2359–2373.Crossref, Google Scholar
• Rinott Y (1978) On two-stage selection procedures and related probability-inequalities. Comm. Statist. Theory Methods 7(8):799–811.Crossref, Google Scholar
• Zhong Y, Hong LJ (2017) A new framework of designing sequential ranking-and-selection procedures. Chan WKV, D’Ambrogio A, Zacharewicz G, Mustafee N, Wainer GA, Page E, eds. Proc. 2017 Winter Simulation Conf. (IEEE, Piscataway, NJ), 2237–2244.Google Scholar