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.

Paper doi abstract bibtex

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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