×

Efficient simulation budget allocation for subset selection using regression metamodels. (English) Zbl 1429.93223

Summary: This research considers the ranking and selection (R&S) problem of selecting the optimal subset from a finite set of alternative designs. Given the total simulation budget constraint, we aim to maximize the probability of correctly selecting the top-\(m\) designs. In order to improve the selection efficiency, we incorporate the information from across the domain into regression metamodels. In this research, we assume that the mean performance of each design is approximately quadratic. To achieve a better fit of this model, we divide the solution space into adjacent partitions such that the quadratic assumption can be satisfied within each partition. Using the large deviation theory, we propose an approximately optimal simulation budget allocation rule in the presence of partitioned domains. Numerical experiments demonstrate that our approach can enhance the simulation efficiency significantly.

MSC:

93C65 Discrete event control/observation systems
62P30 Applications of statistics in engineering and industry; control charts
93-10 Mathematical modeling or simulation for problems pertaining to systems and control theory
PDFBibTeX XMLCite
Full Text: DOI arXiv

References:

[1] Brantley, M. W.; Lee, L. H.; Chen, C.-H.; Chen, A., Efficient simulation budget allocation with regression, IIE Transactions, 45, 3, 291-308 (2013)
[2] Brantley, M. W.; Lee, L. H.; Chen, C.-H.; Xu, J., An efficient simulation budget allocation method incorporating regression for partitioned domains, Automatica, 50, 5, 1391-1400 (2014) · Zbl 1296.93176
[3] Burden, R. L.; Faires, J., Numerical analysis. 2001, Brooks/Cole, USA (2001)
[4] Chen, C.-H.; He, D.; Fu, M.; Lee, L. H., Efficient simulation budget allocation for selecting an optimal subset, INFORMS Journal on Computing, 20, 4, 579-595 (2008)
[5] Chen, C. H.; Lin, J.; Yücesan, E.; Chick, S. E., Simulation budget allocation for further enhancing the efficiency of ordinal optimization, Discrete Event Dynamic Systems, 10, 3, 251-270 (2000) · Zbl 0970.90014
[6] Gao, S.; Chen, W., Efficient subset selection for the expected opportunity cost, Automatica, 59, 19-26 (2015) · Zbl 1326.93083
[7] Gao, S.; Chen, W., A new budget allocation framework for selecting top simulated designs, IIE Transactions, 48, 9, 855-863 (2016)
[8] Gao, S.; Chen, W.; Shi, L., A new budget allocation framework for the expected opportunity cost, Operations Research, 65, 3, 787-803 (2017) · Zbl 1407.91142
[9] Gao, F.; Gao, S.; Xiao, H.; Shi, Z., Advancing constrained ranking and selection with regression in partitioned domains, IEEE Transactions on Automation Science and Engineering, 16, 1, 382-391 (2018)
[10] Gao, S.; Shi, L., Selecting the best simulated design with the expected opportunity cost bound, IEEE Transactions on Automatic Control, 60, 10, 2785-2790 (2015) · Zbl 1360.62080
[11] Gao, S.; Xiao, H.; Zhou, E.; Chen, W., Robust ranking and selection with optimal computing budget allocation, Automatica, 81, 30-36 (2017) · Zbl 1372.93217
[12] De la Garza, A., Spacing of information in polynomial regression, The Annals of Mathematical Statistics, 25, 1, 123-130 (1954) · Zbl 0055.13206
[13] He, D.; Chick, S. E.; Chen, C.-H., The opportunity cost and OCBA selection procedures in ordinal optimization, IEEE Transactions on Systems, Man, and Cybernetics-Part C, 37, 5, 951-961 (2007)
[14] Kiefer, J., Optimum experimental designs, Journal of the Royal Statistical Society. Series B. Statistical Methodology, 272-319 (1959) · Zbl 0108.15303
[15] Kim, S. H.; Nelson, B. L., A fully sequential procedure for indifference-zone selection in simulation, ACM Transactions on Modeling and Computer Simulation, 11, 3, 251-273 (2001) · Zbl 1490.62207
[16] Lee, L. H.; Chen, C.; Chew, E. P.; Li, J.; Pujowidianto, N. A.; Zhang, S., A review of optimal computing budget allocation algorithms for simulation optimization problem, International Journal of Operations Research, 7, 2, 19-31 (2010)
[17] McConnell, J. J.; Servaes, H., Additional evidence on equity ownership and corporate value, Journal of Financial economics, 27, 2, 595-612 (1990)
[18] Peng, Y.; Chen, C.-H.; Fu, M. C.; Hu, J.-Q., Efficient simulation resource sharing and allocation for selecting the best, IEEE Transactions on Automatic Control, 58, 4, 1017-1023 (2013) · Zbl 1369.90199
[19] Xiao, H.; Lee, L. H.; Chen, C.-H., Optimal budget allocation rule for simulation optimization using quadratic regression in partitioned domains, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 45, 7, 1047-1062 (2015)
[20] Xu, J.; Huang, E.; Chen, C.-H.; Lee, L. H., Simulation optimization: A review and exploration in the new era of cloud computing and big data, Asia-Pacific Journal of Operational Research, 32, 03, 1-34 (2015) · Zbl 1318.68186
[21] Xu, J.; Huang, E.; Hsieh, L.; Lee, L. H.; Jia, Q. S.; Chen, C.-H., Simulation optimization in the era of industrial 4.0 and the industrial internet, Journal of Simulation, 10, 4, 310-320 (2016)
[22] Zhang, S.; Lee, L. H.; Chew, E. P.; Xu, J.; Chen, C.-H., A simulation budget allocation procedure for enhancing the efficiency of optimal subset selection, IEEE Transactions on Automatic Control, 61, 1, 62-75 (2016) · Zbl 1359.90085
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.