The Effect of Increment in Simulation Samples on a Combined Selection Procedure
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.
[1] S.H. Kim and B.L. Nelson, "Selecting the best system," Operations
Research and Management Science, Chapter 17, pp. 501-534, 2006.
[2] 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.
[3] 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.
[4] 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.
[5] 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.
[6] 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.
[7] S.S. Gupta, "On some multiple decision (selection and ranking) rules,"
Technometrics, vol. 7, no. 2, pp. 225-245, 1965.
[8] D.W. Sullivan and J.R. Wilson, "Restricted subset selection procedures
for simulation," Operations Research, vol. 37, pp. 52-71, 1989.
[9] Y. Rinott, "On two-stage selection procedures and related probabilityinequalities,"
Communications in Statistics: Theory and Methods, vol.
A7, pp. 799-811, 1978.
[10] 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.
[11] 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.
[12] 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.
[13] 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.
[14] D. Goldsman and B.L. Nelson, "Ranking, selection and multiple comparisons
in computer simulation," Proceedings of the 1994 Winter Simulation
Conference, pp. 192-199.
[15] S.H. Kim and B.L. Nelson, "Recent advances in ranking and selection,"
Proceedings of the 2007 Winter Simulation Conference, pp. 162-172.
[16] R.R. Wilcox, "A table for Rinott-s selection procedure," Journal of
Quality Technology, vol. 16, pp. 97-100, 1984.
[17] 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.
[18] S.E. Chick and Y. Wu, "Selection procedures with frequentist expected
opportunity cost bounds," Operations Research, vol. 53 no. 5,pp. 867-
878, 2005
[1] S.H. Kim and B.L. Nelson, "Selecting the best system," Operations
Research and Management Science, Chapter 17, pp. 501-534, 2006.
[2] 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.
[3] 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.
[4] 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.
[5] 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.
[6] 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.
[7] S.S. Gupta, "On some multiple decision (selection and ranking) rules,"
Technometrics, vol. 7, no. 2, pp. 225-245, 1965.
[8] D.W. Sullivan and J.R. Wilson, "Restricted subset selection procedures
for simulation," Operations Research, vol. 37, pp. 52-71, 1989.
[9] Y. Rinott, "On two-stage selection procedures and related probabilityinequalities,"
Communications in Statistics: Theory and Methods, vol.
A7, pp. 799-811, 1978.
[10] 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.
[11] 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.
[12] 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.
[13] 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.
[14] D. Goldsman and B.L. Nelson, "Ranking, selection and multiple comparisons
in computer simulation," Proceedings of the 1994 Winter Simulation
Conference, pp. 192-199.
[15] S.H. Kim and B.L. Nelson, "Recent advances in ranking and selection,"
Proceedings of the 2007 Winter Simulation Conference, pp. 162-172.
[16] R.R. Wilcox, "A table for Rinott-s selection procedure," Journal of
Quality Technology, vol. 16, pp. 97-100, 1984.
[17] 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.
[18] S.E. Chick and Y. Wu, "Selection procedures with frequentist expected
opportunity cost bounds," Operations Research, vol. 53 no. 5,pp. 867-
878, 2005
@article{"International Journal of Engineering, Mathematical and Physical Sciences:55232", author = "Mohammad H. Almomani and Rosmanjawati Abdul Rahman", title = "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.", keywords = "Indifference-Zone, Optimal Computing Budget Allocation, Ordinal Optimization, Ranking and Selection, Subset Selection.", volume = "5", number = "2", pages = "103-5", }
