A Classification Method for Ranking and Selection with Covariates

Gregory Keslin, Barry L Nelson, Matthew Plumlee, Bernardo K. Pagnoncelli, Hamed Rahimian

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


Ranking & selection (R&S) procedures are simulation-optimization algorithms for making one-time decisions among a finite set of alternative system designs or feasible solutions with a statistical assurance of a good selection. R&S with covariates (R&S+C) extends the paradigm to allow the optimal selection to depend on contextual information that is obtained just prior to the need for a decision. The dominant approach for solving such problems is to employ offline simulation to create metamodels that predict the performance of each system or feasible solution as a function of the covariate. This paper introduces a fundamentally different approach that solves individual R&S problems offline for various values of the covariate, and then treats the real-time decision as a classification problem: given the covariate information, which system is a good solution? Our approach exploits the availability of efficient R&S procedures, requires milder assumptions than the metamodeling paradigm to provide strong guarantees, and can be more efficient.

Original languageEnglish (US)
Title of host publicationProceedings of the 2022 Winter Simulation Conference, WSC 2022
EditorsB. Feng, G. Pedrielli, Y. Peng, S. Shashaani, E. Song, C.G. Corlu, L.H. Lee, E.P. Chew, T. Roeder, P. Lendermann
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages12
ISBN (Electronic)9798350309713
StatePublished - 2022
Event2022 Winter Simulation Conference, WSC 2022 - Guilin, China
Duration: Dec 11 2022Dec 14 2022

Publication series

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


Conference2022 Winter Simulation Conference, WSC 2022

ASJC Scopus subject areas

  • Software
  • Modeling and Simulation
  • Computer Science Applications


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