Distributionally Robust Selection of the Best

Published Online:https://doi.org/10.1287/mnsc.2018.3213

References

  • Asmussen S (2003) Applied Probability and Queues, 2nd ed. (Springer, New York).Google Scholar
  • Barton RR, Schruben LW (2001) Resampling methods for input modeling. Proc. 2001 Winter Simulation Conf. (IEEE, Piscataway, NJ), 372–378.CrossrefGoogle Scholar
  • Barton RR, Nelson BL, Xie W (2014) Quantifying input uncertainty via simulation confidence intervals. INFORMS J. Comput. 26(1):74–87.LinkGoogle Scholar
  • 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.CrossrefGoogle Scholar
  • Ben-Tal A, El Ghaoui L, Nemirovski A (2009) Robust Optimization (Princeton University Press, Princeton, NJ).CrossrefGoogle Scholar
  • Ben-Tal A, den Hertog D, De Waegenaere A, Melenberg B, Rennen G (2013) Robust solutions of optimization problems affected by uncertain probabilities. Management Sci. 59(2):341–357.LinkGoogle Scholar
  • Brown L, Gans N, Mandelbaum A, Sakov A, Shen H, Zeltyn S, Zhao L (2005) Statistical analysis of a telephone call center: A queueing-science perspective. J. Amer. Statist. Assoc. 100(1):36–50.CrossrefGoogle Scholar
  • Cheng RCH, Holland W (1997) Sensitivity of computer simulation experiments to errors in input data. J. Statist. Comput. Simul. 57(1–4):219–241.CrossrefGoogle Scholar
  • Chick SE (2001) Input distribution selection for simulation experiments: Accounting for input uncertainty. Oper. Res. 49(5):744–758.LinkGoogle Scholar
  • Chick SE, Frazier P (2012) Sequential sampling with economics of selection procedures. Management Sci. 58(3):550–569.LinkGoogle Scholar
  • Chick SE, Wu Y (2005) Selection procedures with frequentist expected opportunity cost bounds. Oper. Res. 53(5):867–878.LinkGoogle 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.LinkGoogle Scholar
  • Corlu CG, Biller B (2013) A subset selection procedure under input parameter uncertainty. Proc. 2013 Winter Simulation Conf. (IEEE, Piscataway, NJ), 463–473.CrossrefGoogle Scholar
  • Corlu CG, Biller B (2015) Subset selection for simulations accounting for input uncertainty. Proc. 2015 Winter Simulation Conf. (IEEE, Piscataway, NJ), 437–446.CrossrefGoogle Scholar
  • Delage E, Ye Y (2010) Distributionally robust optimization under moment uncertainty with application to data-driven problems. Oper. Res. 58(3):595–612.LinkGoogle Scholar
  • Dudewicz EJ (1969) An approximation to the sample size in selection problems. Ann. Math. Statist. 40(2):492–497.CrossrefGoogle Scholar
  • Ellsberg D (1961) Risk, ambiguity, and the Savage axioms. Quart. J. Econom. 75(4):643–669.CrossrefGoogle Scholar
  • Epstein LG (1999) A definition of uncertainty aversion. Rev. Econom. Stud. 66(3):579–608.CrossrefGoogle Scholar
  • Fan W, Hong LJ, Nelson BL (2016) Indifference-zone-free selection of the best. Oper. Res. 64(6):1499–1514.LinkGoogle Scholar
  • Fan W, Hong LJ, Zhang X (2013) Robust selection of the best. Proc. 2013 Winter Simulation Conf. (IEEE, Piscataway, NJ), 868–876.CrossrefGoogle Scholar
  • Frazier P (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.LinkGoogle Scholar
  • Frazier P, Powell W, Dayanik S (2009) The knowledge-gradient policy for correlated normal beliefs. INFORMS J. Comput. 21(4):599–613.LinkGoogle Scholar
  • Gilboa I, Schmeidler D (1989) Maxmin expected utility with non-unique prior. J. Math. Econom. 18(2):141–153.CrossrefGoogle Scholar
  • Gupta D, Denton B (2008) Appointment scheduling in health care: Challenges and opportunities. IIE Trans. 40(9):800–819.CrossrefGoogle 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 C: Appl. Rev. 37(5):951–961.CrossrefGoogle Scholar
  • Henderson SG (2003) Input model uncertainty: Why do we care and what should we do about it? Proc. 2003 Winter Simulation Conf. (IEEE, Piscataway, NJ), 90–100.CrossrefGoogle 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.CrossrefGoogle Scholar
  • Hu Z, Hong LJ (2015) Robust simulation of stochastic systems with input uncertainties modeled by statistical divergences. Proc. 2015 Winter Simulation Conf. (IEEE, Piscataway, NJ), 643–654.CrossrefGoogle Scholar
  • Hu Z, Cao J, Hong LJ (2012) Robust simulation of global warming policies using the dice model. Management Sci. 58(12):2190–2206.LinkGoogle Scholar
  • Kelton WD, Sadowski RP, Swets NB (2009) Simulation with Arena, 5th ed. (McGraw-Hill Education, New York).Google Scholar
  • Kim SH, Nelson BL (2001) A fully sequential procedure for indifference-zone selection in simulation. ACM Trans. Model. Comput. Simul. 11(3):251–273.CrossrefGoogle Scholar
  • Kim SH, Nelson BL (2006) Selecting the best system. Henderson SG, Nelson BL, eds. Simulation, Handbooks in Operations Research and Management Science, vol. 13 (Elsevier, Amsterdam), 501–534.CrossrefGoogle Scholar
  • Kong Q, Lee CY, Teo CP, Zheng Z (2013) Scheduling arrivals to stochastic service delivery system using copositive cones. Oper. Res. 61(3):711–726.LinkGoogle Scholar
  • Kong Q, Lee CY, Teo CP, Zheng Z (2016) Appointment sequencing: Why the smallest-variance-first rule may not be optimal. Eur. J. Oper. Res. 255(3):809–821.CrossrefGoogle Scholar
  • Macario A (2010) Is it possible to predict how long a surgery will last? Medscape (July 14), https://www.medscape.com/viewarticle/724756.Google Scholar
  • Mak HY, Rong Y, Zhang J (2015) Appointment scheduling with limited distributional information. Management Sci. 59(7):1557–1575.LinkGoogle Scholar
  • Ni EC, Ciocan DF, Henderson SG, Hunter SR (2017) Efficient ranking and selection in parallel computing environments. Oper. Res. 65(3):821–836.LinkGoogle Scholar
  • Perng SK (1969) A comparison of the asymptotic expected sample sizes of two sequential procedures for ranking problem. Ann. Math. Statist. 40(6):2198–2202.CrossrefGoogle Scholar
  • Qi J (2017) Mitigating delays and unfairness in appointment systems. Management Sci. 63(2):566–583.LinkGoogle Scholar
  • Rinott Y (1978) On two-stage selection procedures and related probability-inequalities. Comm. Statist. Theoret. Methods 7(8):799–811.CrossrefGoogle Scholar
  • Song E, Nelson BL, Hong LJ (2015) Input uncertainty and indifference-zone ranking & selection. Proc. 2015 Winter Simulation Conf. (IEEE, Piscataway, NJ), 414–424.CrossrefGoogle Scholar
  • Strum D, May J, Vargas L (2000) Modeling the uncertainty of surgical procedure times: Comparison of the log-normal and normal models. Anesthesiology 92(4):1160–1167.CrossrefGoogle Scholar
  • Xie W, Nelson BL, Barton RR (2014) A Bayesian framework for quantifying uncertainty in stochastic simulation. Oper. Res. 62(6):1439–1452.LinkGoogle Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.