Selecting the Best Simulated System: Thinking Differently About an Old Problem

Barry L. Nelson*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The methods known collectively as “ranking & selection” have been a theoretical and practical success story for the optimization of simulated stochastic systems: they are widely used in practice, have been implemented in commercial simulation software, and research has made them more and more statistically efficient. However, “statistically efficient” has meant minimizing the number of simulation-generated observations required to make a selection, or maximizing the strength of the inference given a budget of observations. Exploiting high-performance computing, and specifically the capability to simulate many feasible solutions in parallel, has challenged the ranking & selection paradigm. In this paper we review the challenge and suggest an entirely different approach.

Original languageEnglish (US)
Title of host publicationMonte Carlo and Quasi-Monte Carlo Methods, MCQMC 2018
EditorsBruno Tuffin, Pierre L’Ecuyer
PublisherSpringer
Pages69-79
Number of pages11
ISBN (Print)9783030434649
DOIs
StatePublished - 2020
Event13th International Conference on Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing, MCQMC 2018 - Rennes, France
Duration: Jul 1 2018Jul 6 2018

Publication series

NameSpringer Proceedings in Mathematics and Statistics
Volume324
ISSN (Print)2194-1009
ISSN (Electronic)2194-1017

Conference

Conference13th International Conference on Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing, MCQMC 2018
CountryFrance
CityRennes
Period7/1/187/6/18

Keywords

  • Parallel simulation
  • Ranking & selection
  • Simulation optimization
  • Stochastic simulation

ASJC Scopus subject areas

  • Mathematics(all)

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  • Cite this

    Nelson, B. L. (2020). Selecting the Best Simulated System: Thinking Differently About an Old Problem. In B. Tuffin, & P. L’Ecuyer (Eds.), Monte Carlo and Quasi-Monte Carlo Methods, MCQMC 2018 (pp. 69-79). (Springer Proceedings in Mathematics and Statistics; Vol. 324). Springer. https://doi.org/10.1007/978-3-030-43465-6_3