Commenced in January 2007
Paper Count: 30999
The Effect of Increment in Simulation Samples on a Combined Selection Procedure
Abstract:Statistical selection procedures are used to select the best simulated system from a finite set of alternatives. In this paper, we present a procedure that can be used to select the best system when the number of alternatives is large. The proposed procedure consists a combination between Ranking and Selection, and Ordinal Optimization procedures. In order to improve the performance of Ordinal Optimization, Optimal Computing Budget Allocation technique is used to determine the best simulation lengths for all simulation systems and to reduce the total computation time. We also argue the effect of increment in simulation samples for the combined procedure. The results of numerical illustration show clearly the effect of increment in simulation samples on the proposed combination of selection procedure.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1332496Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 947
 S.H. Kim and B.L. Nelson, "Selecting the best system," Operations Research and Management Science, Chapter 17, pp. 501-534, 2006.
 Y.C. Ho, R.S. Sreenivas and P. Vakili, "Ordinal optimization of DEDS," Journal of Discrete Event Dynamic System, vol. 2, pp. 61-88, 1992.
 M.H. Almomani and R. Abdul Rahman, "Selecting a good stochastic system for the large number of alternatives," Communications in Statistics- Simulation and Computation, submitted for publication.
 C.H. Chen, E. Y┬¿ucesan and S.E. Chick, "Simulation budget allocation for further enhancing the efficiency of ordinal optimization," Discrete Event Dynamic Systems, vol. 10, no. 3, pp. 251-270, 2000.
 C.H. Chen, C.D. Wu and L. Dai, "Ordinal comparison of heuristic algorithms using stochastic optimization," IEEE Transaction on Robotics and Automation, vol. 15, no. 1, pp. 44-56, 1999.
 C.H. Chen, "An effective approach to smartly allocated computing budget for discrete event simulation," IEEE Conference on Decision and control, vol. 34, pp. 2598-2605, 1995.
 S.S. Gupta, "On some multiple decision (selection and ranking) rules," Technometrics, vol. 7, no. 2, pp. 225-245, 1965.
 D.W. Sullivan and J.R. Wilson, "Restricted subset selection procedures for simulation," Operations Research, vol. 37, pp. 52-71, 1989.
 Y. Rinott, "On two-stage selection procedures and related probabilityinequalities," Communications in Statistics: Theory and Methods, vol. A7, pp. 799-811, 1978.
 A.C. Tamhane and R.E. Bechhofer, "A two-stage minimax procedure with screening for selecting the largest normal mean," Communications in Statistics: Theory and Methods, vol. A6, pp. 1003-1033, 1977.
 B.L. Nelson, J. Swann, D. Goldsman and W. Song, "Simple procedures for selecting the best simulated system when the number of alternatives is large," Operations Research, vol. 49, pp. 950-963, 2001.
 M.H. Alrefaei and M.H. Almomani, "Subset selection of best simulated systems," Journal of the Franklin Institute, vol. 344, no. 5, pp. 495-506, 2007.
 R.E. Bechhofer, T.J. Santner and D.M. Goldsman, Design and Analysis of Experiments for Statistical Selection, Screening, and Multiple Comparisons. New York:Wiley, 1995.
 D. Goldsman and B.L. Nelson, "Ranking, selection and multiple comparisons in computer simulation," Proceedings of the 1994 Winter Simulation Conference, pp. 192-199.
 S.H. Kim and B.L. Nelson, "Recent advances in ranking and selection," Proceedings of the 2007 Winter Simulation Conference, pp. 162-172.
 R.R. Wilcox, "A table for Rinott-s selection procedure," Journal of Quality Technology, vol. 16, pp. 97-100, 1984.
 D. He, S.E. Chick and C.H. Chen, "Opportunity cost and OCBA selection procedures in ordinal optimization for a fixed number of alternative systems," IEEE Transactions on Systems, vol. 37, pp. 951-961, 2007.
 S.E. Chick and Y. Wu, "Selection procedures with frequentist expected opportunity cost bounds," Operations Research, vol. 53 no. 5,pp. 867- 878, 2005