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A Bayesian approach to the triage problem with imperfect classification

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A Bayesian approach to the triage problem with imperfect classification. / Li, Dong; Glazebrook, K D.
In: European Journal of Operational Research, Vol. 215, No. 1, 16.11.2011, p. 169-180.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Li, D & Glazebrook, KD 2011, 'A Bayesian approach to the triage problem with imperfect classification', European Journal of Operational Research, vol. 215, no. 1, pp. 169-180. https://doi.org/10.1016/j.ejor.2011.05.044

APA

Li, D., & Glazebrook, K. D. (2011). A Bayesian approach to the triage problem with imperfect classification. European Journal of Operational Research, 215(1), 169-180. https://doi.org/10.1016/j.ejor.2011.05.044

Vancouver

Li D, Glazebrook KD. A Bayesian approach to the triage problem with imperfect classification. European Journal of Operational Research. 2011 Nov 16;215(1):169-180. doi: 10.1016/j.ejor.2011.05.044

Author

Li, Dong ; Glazebrook, K D. / A Bayesian approach to the triage problem with imperfect classification. In: European Journal of Operational Research. 2011 ; Vol. 215, No. 1. pp. 169-180.

Bibtex

@article{a9a8b6cb9472465fafc98d95b9f0349e,
title = "A Bayesian approach to the triage problem with imperfect classification",
abstract = "A collection of jobs (or customers, or patients) wait impatiently for service. Each has a random lifetime during which it is available for service. Should this lifetime expire before its service starts then it leaves unserved. Limited resources mean that it is only possible to serve one job at a time. We wish to schedule the jobs for service to maximise the total number served. In support of this objective all jobs are subject to an initial triage, namely an assessment of both their urgency and of their service requirement. This assessment is subject to error. We take a Bayesian approach to the uncertainty generated by error prone triage and discuss the design of heuristic policies for scheduling jobs for service to maximise the Bayes{\textquoteright} return (mean number of jobs served). We identify problem features for which a high price is paid in number of services lost for poor initial triage and for which improvements in initial job assessment yield significant improvements in service outcomes. An analytical upper bound for the cost of imperfect classification is developed for exponentially distributed lifetime cases.",
keywords = "Dynamic programming, Bayes sequential decision problem , Imperfect classification , Stochastic scheduling , Optimal service policy",
author = "Dong Li and Glazebrook, {K D}",
year = "2011",
month = nov,
day = "16",
doi = "10.1016/j.ejor.2011.05.044",
language = "English",
volume = "215",
pages = "169--180",
journal = "European Journal of Operational Research",
issn = "0377-2217",
publisher = "Elsevier Science B.V.",
number = "1",

}

RIS

TY - JOUR

T1 - A Bayesian approach to the triage problem with imperfect classification

AU - Li, Dong

AU - Glazebrook, K D

PY - 2011/11/16

Y1 - 2011/11/16

N2 - A collection of jobs (or customers, or patients) wait impatiently for service. Each has a random lifetime during which it is available for service. Should this lifetime expire before its service starts then it leaves unserved. Limited resources mean that it is only possible to serve one job at a time. We wish to schedule the jobs for service to maximise the total number served. In support of this objective all jobs are subject to an initial triage, namely an assessment of both their urgency and of their service requirement. This assessment is subject to error. We take a Bayesian approach to the uncertainty generated by error prone triage and discuss the design of heuristic policies for scheduling jobs for service to maximise the Bayes’ return (mean number of jobs served). We identify problem features for which a high price is paid in number of services lost for poor initial triage and for which improvements in initial job assessment yield significant improvements in service outcomes. An analytical upper bound for the cost of imperfect classification is developed for exponentially distributed lifetime cases.

AB - A collection of jobs (or customers, or patients) wait impatiently for service. Each has a random lifetime during which it is available for service. Should this lifetime expire before its service starts then it leaves unserved. Limited resources mean that it is only possible to serve one job at a time. We wish to schedule the jobs for service to maximise the total number served. In support of this objective all jobs are subject to an initial triage, namely an assessment of both their urgency and of their service requirement. This assessment is subject to error. We take a Bayesian approach to the uncertainty generated by error prone triage and discuss the design of heuristic policies for scheduling jobs for service to maximise the Bayes’ return (mean number of jobs served). We identify problem features for which a high price is paid in number of services lost for poor initial triage and for which improvements in initial job assessment yield significant improvements in service outcomes. An analytical upper bound for the cost of imperfect classification is developed for exponentially distributed lifetime cases.

KW - Dynamic programming

KW - Bayes sequential decision problem

KW - Imperfect classification

KW - Stochastic scheduling

KW - Optimal service policy

U2 - 10.1016/j.ejor.2011.05.044

DO - 10.1016/j.ejor.2011.05.044

M3 - Journal article

VL - 215

SP - 169

EP - 180

JO - European Journal of Operational Research

JF - European Journal of Operational Research

SN - 0377-2217

IS - 1

ER -