The knowledge-gradient stopping rule for ranking and selection

Peter Frazier, Warren B. Powell

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

18 Scopus citations

Abstract

We consider the ranking and selection of normal means in a fully sequential Bayesian context. By considering the sampling and stopping problems jointly rather than separately, we derive a new composite stopping/sampling rule. The sampling component of the derived composite rule is the same as the previously introduced LL1 sampling rule, but the stopping rule is new. This new stopping rule significantly improves the performance of LL1 as compared to its performance under the best other generally known adaptive stopping rule, EOC Bonf, outperforming it in every case tested.

Original languageEnglish (US)
Title of host publicationProceedings of the 2008 Winter Simulation Conference, WSC 2008
Pages305-312
Number of pages8
DOIs
StatePublished - Dec 1 2008
Event2008 Winter Simulation Conference, WSC 2008 - Miami, FL, United States
Duration: Dec 7 2008Dec 10 2008

Publication series

NameProceedings - Winter Simulation Conference
ISSN (Print)0891-7736

Other

Other2008 Winter Simulation Conference, WSC 2008
CountryUnited States
CityMiami, FL
Period12/7/0812/10/08

All Science Journal Classification (ASJC) codes

  • Software
  • Modeling and Simulation
  • Computer Science Applications

Fingerprint Dive into the research topics of 'The knowledge-gradient stopping rule for ranking and selection'. Together they form a unique fingerprint.

  • Cite this

    Frazier, P., & Powell, W. B. (2008). The knowledge-gradient stopping rule for ranking and selection. In Proceedings of the 2008 Winter Simulation Conference, WSC 2008 (pp. 305-312). [4736082] (Proceedings - Winter Simulation Conference). https://doi.org/10.1109/WSC.2008.4736082