A Classification Method for Ranking and Selection with Covariates

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

doi:10.1109/WSC57314.2022.10015235

A Classification Method for Ranking and Selection with Covariates

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

Industrial Engineering and Management Sciences

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

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 language	English (US)
Title of host publication	Proceedings of the 2022 Winter Simulation Conference, WSC 2022
Editors	B. Feng, G. Pedrielli, Y. Peng, S. Shashaani, E. Song, C.G. Corlu, L.H. Lee, E.P. Chew, T. Roeder, P. Lendermann
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	156-167
Number of pages	12
ISBN (Electronic)	9798350309713
DOIs	https://doi.org/10.1109/WSC57314.2022.10015235
State	Published - 2022
Event	2022 Winter Simulation Conference, WSC 2022 - Guilin, China Duration: Dec 11 2022 → Dec 14 2022

Publication series

Name	Proceedings - Winter Simulation Conference
Volume	2022-December
ISSN (Print)	0891-7736

Conference

Conference	2022 Winter Simulation Conference, WSC 2022
Country/Territory	China
City	Guilin
Period	12/11/22 → 12/14/22

ASJC Scopus subject areas

Software
Modeling and Simulation
Computer Science Applications

Access to Document

10.1109/WSC57314.2022.10015235

Cite this

Keslin, G., Nelson, B. L., Plumlee, M., Pagnoncelli, B. K., & Rahimian, H. (2022). A Classification Method for Ranking and Selection with Covariates. In B. Feng, G. Pedrielli, Y. Peng, S. Shashaani, E. Song, C. G. Corlu, L. H. Lee, E. P. Chew, T. Roeder, & P. Lendermann (Eds.), Proceedings of the 2022 Winter Simulation Conference, WSC 2022 (pp. 156-167). (Proceedings - Winter Simulation Conference; Vol. 2022-December). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/WSC57314.2022.10015235

Keslin, Gregory ; Nelson, Barry L ; Plumlee, Matthew et al. / A Classification Method for Ranking and Selection with Covariates. Proceedings of the 2022 Winter Simulation Conference, WSC 2022. editor / B. Feng ; G. Pedrielli ; Y. Peng ; S. Shashaani ; E. Song ; C.G. Corlu ; L.H. Lee ; E.P. Chew ; T. Roeder ; P. Lendermann. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 156-167 (Proceedings - Winter Simulation Conference).

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title = "A Classification Method for Ranking and Selection with Covariates",

abstract = "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.",

author = "Gregory Keslin and Nelson, {Barry L} and Matthew Plumlee and Pagnoncelli, {Bernardo K.} and Hamed Rahimian",

note = "Funding Information: This research was partially supported by NSF Grant Nos. DMS-1854562 and DMS-1953111. Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 Winter Simulation Conference, WSC 2022 ; Conference date: 11-12-2022 Through 14-12-2022",

year = "2022",

doi = "10.1109/WSC57314.2022.10015235",

language = "English (US)",

series = "Proceedings - Winter Simulation Conference",

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pages = "156--167",

editor = "B. Feng and G. Pedrielli and Y. Peng and S. Shashaani and E. Song and C.G. Corlu and L.H. Lee and E.P. Chew and T. Roeder and P. Lendermann",

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}

Keslin, G, Nelson, BL , Plumlee, M, Pagnoncelli, BK & Rahimian, H 2022, A Classification Method for Ranking and Selection with Covariates. in B Feng, G Pedrielli, Y Peng, S Shashaani, E Song, CG Corlu, LH Lee, EP Chew, T Roeder & P Lendermann (eds), Proceedings of the 2022 Winter Simulation Conference, WSC 2022. Proceedings - Winter Simulation Conference, vol. 2022-December, Institute of Electrical and Electronics Engineers Inc., pp. 156-167, 2022 Winter Simulation Conference, WSC 2022, Guilin, China, 12/11/22. https://doi.org/10.1109/WSC57314.2022.10015235

A Classification Method for Ranking and Selection with Covariates. / Keslin, Gregory; Nelson, Barry L ; Plumlee, Matthew et al.

Proceedings of the 2022 Winter Simulation Conference, WSC 2022. ed. / B. Feng; G. Pedrielli; Y. Peng; S. Shashaani; E. Song; C.G. Corlu; L.H. Lee; E.P. Chew; T. Roeder; P. Lendermann. Institute of Electrical and Electronics Engineers Inc., 2022. p. 156-167 (Proceedings - Winter Simulation Conference; Vol. 2022-December).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Rahimian, Hamed

PY - 2022

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N2 - 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.

AB - 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.

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Keslin G, Nelson BL , Plumlee M, Pagnoncelli BK, Rahimian H. A Classification Method for Ranking and Selection with Covariates. In Feng B, Pedrielli G, Peng Y, Shashaani S, Song E, Corlu CG, Lee LH, Chew EP, Roeder T, Lendermann P, editors, Proceedings of the 2022 Winter Simulation Conference, WSC 2022. Institute of Electrical and Electronics Engineers Inc. 2022. p. 156-167. (Proceedings - Winter Simulation Conference). doi: 10.1109/WSC57314.2022.10015235

A Classification Method for Ranking and Selection with Covariates

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