A New Budget Allocation Framework for the Expected Opportunity Cost

References

• Boyd S, Vandenberghe L (2004) Convex Optimization (Cambridge University Press, New York).Crossref, Google Scholar
• Branke J, Chick SE, Schmidt C (2007) Selecting a selection procedure. Management Sci. 53(11):1916–1932.Link, Google Scholar
• Chen CH, Lee LH (2010) Stochastic Simulation Optimization: An Optimal Computing Budget Allocation (World Scientific Publishing, Singapore).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. Simul. Model. Pract. Th. 11(1):57–74.Crossref, Google Scholar
• Chen CH, He D, Fu M, Lee LH (2008) Efficient simulation budget allocation for selecting an optimal subset. INFORMS J. Comput. 20(4):579–595.Link, Google Scholar
• Chen CH, Lin J, Yücesan E, Chick SE (2000) Simulation budget allocation for further enhancing the efficiency of ordinal optimization. Discrete Event Dyn. S. 10(3):251–270.Crossref, Google Scholar
• Chick SE, Inoue K (2001a) New procedures for identifying the best simulated system using common random numbers. Management Sci. 47(8):1133–1149.Link, Google Scholar
• Chick SE, Inoue K (2001b) New two-stage and sequential procedures for selecting the best simulated system. Oper. Res. 49(5):732–743.Link, Google Scholar
• Chick SE, Wu Y (2005) Selection procedures with frequentist expected opportunity cost bounds. Oper. Res. 53(5):867–878.Link, Google Scholar
• Dembo A, Zeitouni O (1998) Large Deviations Techniques and Applications, 2nd ed. (Springer, New York).Crossref, Google Scholar
• Du D, Pardalos PM (1995) Minimax and Applications (Kluwer, Dordrecht, Netherlands).Crossref, Google Scholar
• Dudewicz EJ, Dalal SR (1975) Allocation of observations in ranking and selection with unequal variances. Sankhya: The Indian Journal of Statistics, Series B 37(1):28–78.Google Scholar
• Gao S, Chen W (2015) Efficient subset selection for the expected opportunity cost. Automatica 59:19–26.Crossref, Google Scholar
• Gao S, Chen W (2017) Efficient feasibility determination with multiple performance measure constraints. IEEE Trans. Automat. Contr. 62(1):113–122.Crossref, Google Scholar
• Gao S, Shi L (2014) An optimal opportunity cost selection procedure for a fixed number of designs. Buckley SJ, Miller JA, eds. Proc. 2014 Winter Simulation Conf., WSC ’14 (IEEE, Piscataway, NJ), 3592–3598.Crossref, Google Scholar
• Gao S, Shi L (2015) Selecting the best simulated design with the expected opportunity cost bound. IEEE Trans. Automat. Contr. 60(10):2785–2790.Crossref, Google Scholar
• Glynn P, Juneja S (2004) A large deviations perspective on ordinal optimization. Proc. 36th Conf. Winter Simulation, WSC ’04 (IEEE, Piscataway, NJ), 577–585.Crossref, Google Scholar
• Glynn P, Juneja S (2011) Ordinal optimization: A nonparametric framework. Jain S, Creasey Jr RR, Himmelspach J, White KP, Fu MC, eds. Proc. 2011 Winter Simulation Conf., WSC ’11 (IEEE, Piscataway, NJ), 4062–4069.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 C 37(5):951–961.Crossref, Google Scholar
• Ho Y, Zhao Q, Jia Q (2007) Ordinal Optimization: Soft Optimization for Hard Problems (Springer, New York).Crossref, Google Scholar
• Hunter SR, Pasupathy R (2013) Optimal sampling laws for stochastically constrained simulation optimization on finite sets. INFORMS J. Comput. 25(3):527–542.Link, Google Scholar
• Kim SH, Nelson BL (2001) A fully sequential procedure for indifference-zone selection in simulation. ACM Trans. Modeling Comput. Simulation 11(3):251–273.Crossref, Google Scholar
• Koening LW, Law AM (1985) A procedure for selecting a subset of size m containing the l best of k independent normal populations, with applications to simulation. Commun. Statist.-Simul. Comput. 14(3):719–734.Crossref, Google Scholar
• Krantz S, Parks H (2012) The Implicit Function Theorem: History, Theory, and Applications (Springer, New York).Google Scholar
• Law AM, Kelton WD (2000) Simulation Modeling and Analysis, 3rd ed. (McGraw-Hill, New York).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.Link, Google Scholar
• Nelson BL, Swann J, Goldsman D, Song W (2001) Simple procedures for selecting the best simulated system when the number of alternatives is large. Oper. Res. 49(6):950–963.Link, Google Scholar
• Pasupathy R, Hunter SR, Pujowidianto NA, Lee LH, Chen C-H (2014) Stochastically constrained ranking and selection via SCORE. ACM Trans. Model. Comput. Simul. 25(1):1–26.Crossref, Google Scholar
• Pujowidianto NA, Lee LH, Chen C-H (2013) Minimizing opportunity cost in selecting the best feasible design. Proc. 2013 Winter Simulation Conf., 898–907.Crossref, Google Scholar
• Rinott Y (1978) On two-stage selection procedures and related probability inequalities. Comm. Statist. A7(8):799–811.Crossref, Google Scholar
• Ryzhov I (2015) Expected improvement is equivalent to OCBA. Proc. 2015 Winter Simulation Conf., 3668–3677.Crossref, Google Scholar
• Shortle J, Chen C-H, Crain B, Brodsky A, Brod D (2012) Optimal splitting for rare-event simulation. IIE Trans. 44(5):352–367.Crossref, Google Scholar
• Szechtman R, Yücesan E (2008) A new perspective on feasibility determination. Proc. 2008 Winter Simulation Conf., 273–280.Crossref, Google Scholar
• Xu J, Huang E, Chen C-H, Lee LH (2015) Simulation optimization: A review and exploration in the new era of cloud computing and big data. Aia Pac. J. Oper. Res. 32(3):1–34.Google Scholar