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View all- Lam HHuschka TChick S(2016)Advanced tutorialProceedings of the 2016 Winter Simulation Conference10.5555/3042094.3042128(178-192)Online publication date: 11-Dec-2016
Ranking and selection meets robust optimization
Article No.: 48, Pages 1 - 11
Abstract
The objective of ranking and selection is to efficiently allocate an information budget among a set of design alternatives with unknown values in order to maximize the decision-maker's chances of discovering the best alternative. The field of robust optimization, however, considers risk-averse decision makers who may accept a suboptimal alternative in order to minimize the risk of a worst-case outcome. We bring these two fields together by defining a Bayesian ranking and selection problem with a robust implementation decision. We propose a new simulation allocation procedure that is risk-neutral with respect to simulation outcomes, but risk-averse with respect to the implementation decision. We discuss the properties of the procedure and present numerical examples illustrating the difference between the risk-averse problem and the more typical risk-neutral problem from the literature.
References
[1]
Bechhofer, R. E., T. J. Santner, and D. M. Goldsman. 1995. Design and Analysis of Experiments for Statistical Selection, Screening and Multiple Comparisons. New York: J. Wiley & Sons.
[2]
Ben-Tal, A., L. El Ghaoui, and A. Nemirovski. 2009. Robust Optimization. Princeton University Press.
[3]
Ben-Tal, A., and A. Nemirovski. 2002. "Robust optimization--methodology and applications". Mathematical Programming 92 (3): 453--480.
[4]
Bertsimas, D., D. B. Brown, and C. Caramanis. 2007. "Theory and Applications of Robust Optimization". SIAM Review 53 (3): 464--501.
[5]
Branke, J., S. Chick, and C. Schmidt. 2007. "Selecting a Selection Procedure". Management Science 53 (12): 1916--1932.
[6]
Chick, S. E. 2006. "Subjective Probability and Bayesian Methodology". In Handbooks of Operations Research and Management Science, vol. 13: Simulation, edited by S. Henderson and B. Nelson, 225--258. North-Holland Publishing, Amsterdam.
[7]
Chick, S. E., J. Branke, and C. Schmidt. 2010. "Sequential Sampling to Myopically Maximize the Expected Value of Information". INFORMS Journal on Computing 22 (1): 71--80.
[8]
Chick, S. E., and P. I. Frazier. 2009, December. "The Conjunction Of The Knowledge Gradient And The Economic Approach To Simulation Selection". In Proceedings of the 2009 Winter Simulation Conference, edited by M. D. Rossetti, R. R. Hill, B. Johansson, A. Dunkin, and R. G. Ingalls, 528--539. Piscataway, New Jersey: Institute of Electrical and Electronics Engineers, Inc.
[9]
Chick, S. E., and N. Gans. 2009. "Economic analysis of simulation selection problems". Management Science 55 (3): 421--437.
[10]
Clark, C. E. 1961. "The greatest of a finite set of random variables". Operations Research 9 (2): 145--162.
[11]
Defourny, B., I. O. Ryzhov, and W. B. Powell. 2012. "The robust approach to simulation selection". Working paper, Princeton University.
[12]
DeGroot, M. H. 1970. Optimal Statistical Decisions. John Wiley and Sons.
[13]
Frazier, P. I., W. B. Powell, and S. Dayanik. 2008. "A knowledge gradient policy for sequential information collection". SIAM Journal on Control and Optimization 47 (5): 2410--2439.
[14]
Gupta, S., and K. Miescke. 1994. "Bayesian look ahead one stage sampling allocations for selecting the largest normal mean". Statistical Papers 35: 169--177.
[15]
Gupta, S., and K. Miescke. 1996. "Bayesian look ahead one-stage sampling allocations for selection of the best population". Journal of Statistical Planning and Inference 54 (2): 229--244.
[16]
Hong, L. J., and B. L. Nelson. 2009, December. "A Brief Introduction To Optimization Via Simulation". In Proceedings of the 2009 Winter Simulation Conference, edited by M. D. Rossetti, R. R. Hill, B. Johansson, A. Dunkin, and R. G. Ingalls, 75--85. Piscataway, New Jersey: Institute of Electrical and Electronics Engineers, Inc.
[17]
Kim, S.-H., and B. L. Nelson. 2001. "A fully sequential procedure for indifference-zone selection in simulation". ACM Transactions on Modeling and Computer Simulation 11 (3): 251--273.
[18]
Kim, S.-H., and B. L. Nelson. 2006. "Selecting the best system". In Handbooks of Operations Research and Management Science, vol. 13: Simulation, edited by S. G. Henderson and B. L. Nelson, 501--534. North-Holland Publishing, Amsterdam.
[19]
Powell, W. B., and I. O. Ryzhov. 2012. Optimal Learning. John Wiley and Sons.
[20]
Waeber, R., P. I. Frazier, and S. G. Henderson. 2010, December. "Performance Measures for Ranking and Selection Procedures". In Proceedings of the 2010 Winter Simulation Conference, edited by B. Johansson, S. Jain, J. Montoya-Torres, J. Hugan, and E. Yücesan, 1235--1245. Piscataway, New Jersey: Institute of Electrical and Electronics Engineers, Inc.
[21]
Wang, H., and S.-H. Kim. 2011. "On the Conservativeness of Fully Sequential Indifference-Zone Procedures". Submitted for publication.
Index Terms
- Ranking and selection meets robust optimization
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- IIE: Institute of Industrial Engineers
- INFORMS-SIM: Institute for Operations Research and the Management Sciences: Simulation Society
- SCS: Shanghai Computer Society
- SIGSIM: ACM Special Interest Group on Simulation and Modeling
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Winter Simulation Conference
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Published: 09 December 2012
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View all- Lam HHuschka TChick S(2016)Advanced tutorialProceedings of the 2016 Winter Simulation Conference10.5555/3042094.3042128(178-192)Online publication date: 11-Dec-2016
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