Please use this identifier to cite or link to this item: https://doi.org/10.1109/WSC.2011.6148116
Title: Optimal sampling laws for constrained simulation optimization on finite sets: The bivariate normal case
Authors: Hunter, S.R.
Chen, C.-H.
Pasupathy, R.
Pujowidianto, N.A.
Lee, L.H. 
Yap, C.M. 
Issue Date: 2011
Source: Hunter, S.R.,Chen, C.-H.,Pasupathy, R.,Pujowidianto, N.A.,Lee, L.H.,Yap, C.M. (2011). Optimal sampling laws for constrained simulation optimization on finite sets: The bivariate normal case. Proceedings - Winter Simulation Conference : 4289-4297. ScholarBank@NUS Repository. https://doi.org/10.1109/WSC.2011.6148116
Abstract: Consider the context of selecting an optimal system from amongst a finite set of competing systems, based on a "stochastic" objective function and subject to a single "stochastic" constraint. In this setting, and assuming the objective and constraint performance measures have a bivariate normal distribution, we present a characterization of the optimal sampling allocation across systems. Unlike previous work on this topic, the characterized optimal allocations are asymptotically exact and expressed explicitly as a function of the correlation between the performance measures. © 2011 IEEE.
Source Title: Proceedings - Winter Simulation Conference
URI: http://scholarbank.nus.edu.sg/handle/10635/72365
ISBN: 9781457721083
ISSN: 08917736
DOI: 10.1109/WSC.2011.6148116
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