Optimal sampling laws for constrained simulation optimization on finite sets: the bivariate normal case

Optimal sampling laws for constrained simulation optimization on finite sets: the bivariate normal case. Hunter, S. R., Pujowidianto, N. A., Chen, C., Lee, L. H., Pasupathy, R., & Yap, C. M. In Jain, S., Creasey, R. R., Himmelspach, J., White, K. P., & Fu, M., editors, Proceedings of the 2011 Winter Simulation Conference, pages 4294–4302, Piscataway, NJ, 2011. Institute of Electrical and Electronics Engineers, Inc.. Finalist, Winter Simulation Conference Best Theoretical Paper Award.

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Consider the context of selecting an optimal system from among 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.

@inproceedings{2011hunpujetalWSC,
	Year = {2011},
	Author = {S. R. Hunter and N. A. Pujowidianto and {C.-H.} Chen and L. H. Lee and R. Pasupathy and C. M. Yap},
	Title = {Optimal sampling laws for constrained simulation optimization on finite sets: the bivariate normal case},
	Booktitle = {Proceedings of the 2011 Winter Simulation Conference},
	Editor = {S. Jain and R. R. Creasey and J. Himmelspach and K. P. White and M. Fu},
	Publisher = {Institute of Electrical and Electronics Engineers, Inc.},
	Address = {Piscataway, NJ},
	Pages = {4294--4302},
	doi = {10.1109/WSC.2011.6148116},
	url_Paper = {http://web.ics.purdue.edu/~hunter63/PAPERS/2011hunpujetalWSC.pdf},
	abstract = {Consider the context of selecting an optimal system from among 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.},
	keywords = {simulation optimization > single-objective > stochastically constrained > ranking and selection},
	bibbase_note = {<span style="color: green">Finalist, Winter Simulation Conference Best Theoretical Paper Award.</span>}}

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