Integration of indifference-zone with multi-objective computing budget allocation

European Journal of Operational Research

Volumn 203, Issue 2, 2010, Pages 419-429

  Teng, Suyan ^a   Lee, Loo Hay ^a   Chew, Ek Peng ^a  

^a NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

Author keywords

Computing budget allocation; Indifference zone; Lagrange relaxation; Multi objective

Indexed keywords

ALLOCATION RULE; COMPUTING BUDGET; COMPUTING BUDGET ALLOCATION; EMPIRICAL RESULTS; INDIFFERENCE-ZONE; LAGRANGE RELAXATION; MULTI-OBJECTIVE; MULTI-OBJECTIVE RANKING; PARETO SET; SIMULATION REPLICATION;

COMPUTER SCIENCE; INTEGRATION; LAGRANGE MULTIPLIERS; MULTIOBJECTIVE OPTIMIZATION;

BUDGET CONTROL;

EID: 70350724410     PISSN: 03772217     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ejor.2009.08.008     Document Type: Article

References (26)

1. Alrefaei M.H., and Alawneh A.J. Selecting the best stochastic system for large scale problems in DEDS. Mathematics and Computers in Simulation 64 (2004) 237-245
2. Butler J., Morrice D.J., and Mullarkey P.W. A multiple attribute utility theory approach to ranking and selection. Management Science 47 6 (2001) 800-816
3. Cai J.W. Some asymptotic results for hypothesis testing in multivariate failure time data. Journal of Statistical Planning and Inference 75 (1999) 211-221
4. Chen, C.H., Chen, H.C., Dai, L., 1996. A gradient approach for smartly allocating computing budget for discrete event simulation. In: Proceedings of the 1996 Winter Simulation Conference. IEEE Press, Piscataway, NJ, pp. 398-405.
5. Chen C.H., Donohue K., Yücesan E., and Lin J. Optimal computing budget allocation for Monte Carlo simulation with application to product design. Simulation Modelling Practice and Theory 11 (2003) 57-74
6. Chen C.H., Lin J.W., Yücesan E., and Chick S.E. Simulation budget allocation for further enhancing the efficiency of ordinal optimization. Discrete Event Dynamic Systems: Theory and Applications 10 (2000) 251-270
7. Chen H.C., Chen C.H., and Yücesan E. Computing efforts allocation for ordinal optimization and discrete event simulation. IEEE Transactions on Automatic Control 45 5 (2000) 960-964
8. Chen, E.J., Chen, C.H., Kelton, W.D., 2004. Optimal computing budget allocation of indifference-zone-selection procedures. Working paper that can be found at http://www.cba.uc.edu/faculty/keltonwd/.
9. Chen E.J., and Kelton W.D. Sequential selection procedures: Using sample means to improve efficiency. European Journal of Operational Research 166 (2005) 133-153
10. Chen, H.C., Dai, L., Chen, C.H., Yücesan, E., 1997. New development of optimal computing budget allocation for discrete event simulation. In: Proceedings of the 1997 Winter Simulation Conference. IEEE Press, Piscataway, NJ, pp. 334-341.
11. Childs A., and Balakrishnan N. Some approximations to multivariate Polya-Eggenberger distributions with applications to hypothesis testing. Communications in Statistics - Simulation and Computation 31 (2002) 213-243
12. Freidlin, R.Z., Assaf, Y., Basser, P.J., 2007. Multivariate hypothesis testing of DTI data for tissue clustering. In: Fourth IEEE International Symposium on Biomedical Imaging. IEEE, Arlington, VA, pp. 776-779.
13. Griswold C.K., Gomulkiewicz R., and Heckman N. Hypothesis testing in comparative and experimental studies of function-valued traits. Evolution 62 (2008) 1229-1242
14. Goldsman, D., Nelson, B.L., 1998. Statistical screening, selection, and multiple comparison procedures in computer simulation. In: Proceedings of the 1998 Winter Simulation Conference. IEEE Press, Piscataway, NJ, pp. 159-166.
15. Gupta A.K., Harrar S.W., and Fujikoshi Y. Asymptotics for testing hypothesis in some multivariate variance components model under non-normality. Journal of Multivariate Analysis 97 (2006) 148-178
16. Kim, S.H., Nelson, B.L., 2003. Selecting the best system: Theory and methods. In: Proceedings of the 2003 Winter Simulation Conference. IEEE Press, Piscataway, NJ, pp. 101-112.
17. Kim, S.H., Nelson, B.L., 2007. Recent advances in ranking and selection. In: Proceedings of the 2007 Winter Simulation Conference. IEEE Press, Piscataway, NJ, pp. 162-172.
18. Lee, L.H., Chew, E.P., Teng, S.Y., Goldsman, D., 2004. Optimal computing budget allocation for multi-objective simulation models. In: Proceedings of the 2004 Winter Simulation Conference. IEEE Press, Piscataway, NJ, pp. 586-594.
19. Lee, L.H., Chew, E.P., Teng, S.Y., Goldsman, D., submitted for publication. Finding the non-dominated Pareto set for multi-objective simulation models. IIE Transactions.
20. Morrice, D.J., Butler, J., Mullarkey, P.W., 1998. An approach to ranking and selection for multiple performance measures. In: Proceedings of the 1998 Winter Simulation Conference. IEEE Press, Piscataway, NJ, pp. 719-725.
21. Nelson B.L., Swann J., Goldsman D., and Song W.M. Simple procedures for selecting the best simulated system when the number of alternatives is large. Operations Research 49 (2001) 950-963
22. Rinott Y. On two-stage selection procedures and related probability-inequalities. Communications in Statistics A7 8 (1978) 799-811
23. Swisher J.R., and Jacobson S.H. Evaluating the design of a family practice healthcare clinic using discrete-event simulation. Health Care Management Science 5 2 (2002) 75-88
24. Swisher J.R., Jacobson S.H., and Yücesan E. Discrete-event simulation optimization using ranking, selection, and multiple comparison procedures: A survey. ACM Transactions on Modeling and Computer Simulation 13 2 (2003) 134-154
25. Teskin O.I., and Sudakov R.S. Multivariate hypothesis testing and its application to quality control. Journal of Mathematical Sciences 39 (1987) 2648-2661
26. Thabane L., and Drekic S. Hypothesis testing for the generalized multivariate modified Bessel model. Journal of Multivariate Analysis 86 (2003) 360-374