Fully sequential procedures for large-scale ranking-and-selection problems in parallel computing environments

Operations Research

Volumn 63, Issue 5, 2015, Pages 1177-1194

  Luo, Jun ^a   Hong, L Jeff ^b   Nelson, Barry L ^c   Wu, Yang ^d  

^a Shanghai Jiao Tong University   (China)

^b CITY UNIVERSITY OF HONG KONG   (Hong Kong)

^c Northwestern University   (United States)

^d ALIBABA GROUP   (China)

Author keywords

Asymptotic validity; Fully sequential procedures; Parallel computing; Statistical issues

Indexed keywords

PERSONAL COMPUTERS; UBIQUITOUS COMPUTING;

ASYMPTOTIC VALIDITY; NUMERICAL EXPERIMENTS; PARALLEL-COMPUTING ENVIRONMENT; RANKING AND SELECTION; SELECTION PROCEDURES; SEQUENTIAL PROCEDURES; SIMULATED ALTERNATIVES; STATISTICAL ISSUES;

PARALLEL PROCESSING SYSTEMS;

EID: 84947925786     PISSN: 0030364X     EISSN: 15265463     Source Type: Journal    
DOI: 10.1287/opre.2015.1413     Document Type: Article

References (40)

1. Amdahl G (1967) Validity of the single processor approach to achieving large-scale computing capabilities. AFIPS Conf. Proc., Vol. 30 (ACM, New York),483-485.
2. Bechhofer RE (1954) A single-sample multiple decision procedure for ranking means of normal populations with known variances. Ann. Math. Statist. 25:16-39.
3. Bechhofer RE, San tner TJ, Goldsman DM (1995) Design and Analysis of Experiments for Statistical Selection, Screening, and Multiple Comparisons (John Wiley and Sons, New York).
4. Branke J, Chick SE, Schmidt C (2007) Selecting a selection procedure. Management Sci. 53(12):1916-1932.
5. Buzacott JA, Shanthikumar JG (1993) Stochastic Models of Manufacturing Systems (Prentice Hall, Englewood Cliffs, NJ).
6. Cario MC, Nelson BL (1998) Numerical methods for fitting and simulating autoregressive-to-Anything processes. INFORMS J. Comput. 10(1):72-81.
7. Chen EJ (2005) Using parallel and distributed computing to increase the capability of selection procedures. Proc. 2005 Winter Simulation Conf. (IEEE, Washington, DC), 723-731.
8. Chen C-H,Lin J,Yücesan E,Chick SE 2000 Simulation budget allocation for further enhancing the efficiency of ordinal optimization Discrete Event Dynamic Systems 10: 251-270.
9. Chick SE, Frazier PI (2012) Sequential sampling with economics of selection procedures. Management Sci. 58(3):550-569.
10. Chick SE, Gans N (2009) Economic analysis of simulation selection problems. Management Sci. 55(3):421-437.
11. Chick SE, Inoue K (2001 a ) New procedures to select the best simulated system using common random numbers. Management Sci. 47(8):1133-1149.
12. Chick SE, Inoue K (2001 b) N ew two-stage and sequential procedures for selecting the best simulated system. Oper. Res. 49(5):732-743.
13. Durrett R (2004) Probability: Theory and Examples, 3rd ed. (Duxbury Press, Pacific Grove, CA).
14. Fabian V (1974) Note on Anderson's sequential procedures with triangular boundary. Ann. Statist. 2:170-176.
15. Foster I (1995) Designing and Building Parallel Programs: Concepts and Tools for Parallel Software Engineering (Addison-Wesley, Reading, MA).
16. Fujimoto RM (1990) Parallel discrete event simulation. Commun. ACM 33:30-53.
17. Fujimoto RM, Malik AW, Park AJ (2010) Parallel and distributed simulation in the cloud. SCS Model. Simulation Magazine 1:1-10.
18. Glynn PW, Heidelberger P (1991) Analysis of parallel replicated simulations under a completion time constraint. ACM Trans. Model. Comput. Simulation 1:3-23.
19. Heidelberger P (1988) Discrete event simulation and parallel processing: statistical properties. SIAM J. Sci. Statist. Comput. 6:1114-1132.
20. Hong LJ (2006) Fully sequential indifference-zone selection procedures with variance-dependent sampling. Naval Res. Logist. 53:464-476.
21. Hong LJ, Nelson BL (2005) The tradeoff between sampling and switching: New sequential procedures for indifference-zone selection. IIE Trans. 37:623-634.
22. Hong LJ, Nelson BL (2007) Selecting the best system when systems are revealed sequentially. IIE Trans. 39:723-734.
23. Hong LJ, Nelson BL (2009) A brief introduction to optimization via simulation. Proc. 2009 Winter Simulation Conf. (IEEE, Washington, DC), 75-85.
24. Hsieh M, Glynn PW (2009) New estimators for parallel steady-state simulations. Proc. 2009 Winter Simulation Conf. (IEEE, Washington, DC), 469-474.
25. Kim S-H, Nelson BL (2001) A fully sequential procedure for indifferencezone selection in simulation. ACM Trans. Modeling Comput. Simulation 11:251-273.
26. Kim S-H, Nel son BL (2006a ) On the asymptotic validity of fully sequential selection procedures for steady-state simulation. Oper. Res. 54(3):475-488.
27. Kim S-H, Nelson BL (2006 b) Selecting the best system. Henderson SG, Nelson BL, eds. Elsevier Handbooks in Operations Research and Management Science: Simulation (Elsevier, Amsterdam), 501-534.
28. Kim S-H, Nelson BL, Wilson JR (2005) Some almost-sure convergence properties useful in sequential analysis. Sequential Anal. 24(4): 411-419.
29. Luo J, Hong LJ (2011) Large-scale ranking and selection using cloud computing. Proc. 2011 Winter Simulation Conf. (IEEE, Washington, DC), 4051-4061.
30. Misra J 1986 Distributed discrete-event simulation. ACM Comput. Surveys 18: 39-65.
31. 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.
32. Ni EC, Hunter SR, Henderson SG (2013) Ranking and selection in a high performance computing environment. Proc. 2013 Winter Simulation Conf. (IEEE, Washington, DC), 833-845.
33. Pichitlamken J, Nelson BL, Hong LJ (2006) A sequential procedure for neighborhood selection-of-the-best in optimization via simulation. Eur. J. Oper. Res. 173:283-298.
34. Pinedo ML (2008) Scheduling: Theory, Algorithms, and Systems, 3rd ed. (Springer, New York).
35. Rinott Y (1978) On two-stage selection procedures and related probability-inequalities. Commun. Statist.-Theory Methods 7:799-811.
36. Silvay LME, Buyya R (1999) Parallel programming models and paradigms. High Performance Cluster Computing: Programming and Applications (Prentice Hall, Upper Saddle River, NJ), 4-27.
37. Stein C (1945) A two-sample test for a linear hypothesis whose power is independent of the variance. Ann. Math. Statist. 16:243-258.
38. Whitt W (2002) Stochastic-Process Limits, Springer Series in Operations Research (Springer, New York).
39. Xu J, Nelson BL, Hong LJ (2010) Industrial strength COMPASS: A comprehensive algorithm and software for optinization via simulation. ACM Trans. Modeling Comput. Simulation 20:1-29.
40. Yücesan E, Luo Y-C, Chen CH, Lee I (2001) Distributed web-based simulation experiments for optimization. Simulation Practice Theory 9:73-90.