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|Title:||Optimal sampling laws for constrained simulation optimization on finite sets: The bivariate normal case|
|Citation:||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|
|Appears in Collections:||Staff Publications|
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