ACM Home Page
Please provide us with feedback. Feedback
The impact of ordinal on response surface methodology
Full text PdfPdf (401 KB)
Source Winter Simulation Conference archive
Proceedings of the 38th conference on Winter simulation table of contents
Monterey, California
SESSION: Analysis methodology b: estimation, queueing, and optimization table of contents
Pages: 406 - 413  
Year of Publication: 2006
ISBN:1-4244-0501-7
Authors
Sara Jian Oon  Raffles Junior College, Singapore
Loo Hay Lee  National University of Singapore, Singapore
Sponsors
IEICE ESS : Institute of Electronics, Information and Communication Engineers, Engineering Sciences Society
IIE : Institute of Industrial Engineers
ASA : American Statistical Association
IEEE-CS\DATC : The IEEE Computer Society
INFORMS-CS : Institute for Operations Research and the Management Sciences-College on Simulation
NIST : National Institute of Standards and Technology
SIGSIM: ACM Special Interest Group on Simulation and Modeling
(SCS) : The Society for Modeling and Simulation International
Publisher
Winter Simulation Conference 
Bibliometrics
Downloads (6 Weeks): 1,   Downloads (12 Months): 9,   Citation Count: 0
Additional Information:

abstract   references   collaborative colleagues  

Tools and Actions: Review this Article  

ABSTRACT

Traditionally, Response Surface Methodology (RSM) is cardinal in nature. Ordinal optimization was only introduced recently. Since ordinal optimization has been proven to be successful in certain applications, this paper aims to investigate whether ordinal optimization improves RSM by developing ordinal RSM and comparing it with cardinal RSM in terms of efficiency, accuracy and consistency. Assuming that the performances of systems can be expressed as functions of their parameters, both ordinal and cardinal RSM are simulated for several simple multivariable mathematical functions and the effectiveness of ordinal RSM evaluated. It was found that ordinal does not always improve RSM, especially in functions which exhibit a large gradient change over a small region.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
1
C. M. Barnhart, J. E. Wieselthier, and A. Ephremides. 1994. Ordinal optimization by means of standard clock simulation and crude analytical models. Proc. 33rd Conf. Decision and Control, Lake Buena Vista, FL, 2645--2647
 
2
 
3
C. G. Cassandras and G. Bao. 1994. A stochastic comparison algorithm for continuous optimization with estimations. Proc. 33rd Conf. Decision and Control, Lake Buena Vista, FL
 
4
 
5
 
6
W.-B. Gong, Y.-C. Ho, and W. Zhai. 1992. Stochastic comparison algorithm for discrete optimization with estimations. Proc. 31st IEEE Conf Decision and Control
 
7
Y.-C. Ho, R. S. Sreenivas, and P. Vakili. 1992. Ordinal Optimization in DEDS. J. Discrete Event Dynamic Syst., 3, 61--68
 
8
Y.-C. Ho and M. Deng. 1994. Large search space problems in ordinal optimization. Proc. Conf. Decision and Control
 
9
Y.-C Ho and M. E. Larson. 1995. Ordinal optimization approach to rare event probability problems. J. Discrete Event Dynamic Syst., 5, 281--301
 
10
Y.-C Ho, C. G. Cassandras, C.-H. Chen, and L.-Y. Dai. 2000. Ordinal Optimization and Simulation, Journal of Operational Research Society
 
11
L. H. Lee, T. W. E. Lau and Y. C. Ho. 1999. Explanation of goal softening in ordinal optimization. IEEE Transactions on Automatic Control, 44 (1), 94--99.
 
12
L. H. Lee, F. H. Abernathy, and Y. C. Ho. 2000. Production scheduling for apparel manufacturing systems. Production Planning and Control, 11 (3), 281--290.
 
13
D. C. Montgomery and G. C. Runger. 1994. Applied Statistics and Probability for Engineers, John Wiley and Sons, Inc, New York
 
14
 
15
Patsis, N. T., C. H. Chen, and M. E. Larson. 1997. SIMD Parallel Discrete Event Dynamic System Simulation. IEEE Trans. on Control Systems Technology, 5 (3), 30--41
 
16
P. Vakili, L. Mollamustaflaglu, and Y.-C. Ho. 1992. Massively parallel simulation of a class of discrete event systems. Proc. IEEE Frontiers MPC Symp., Washington, DC
 
17
X. L. Xie. 1997. Dynamics and convergence rate of ordinal comparison of stochastic discrete event systems. IEEE Trans. Automat. Contr., 42, 586--590
Collaborative Colleagues:
Sara Jian Oon: colleagues
Loo Hay Lee: colleagues