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A Bayesian approach to the triage problem with imperfect classification

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  • Li, Dong
  • Glazebrook, Kevin D.

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' 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.

Suggested Citation

  • Li, Dong & Glazebrook, Kevin D., 2011. "A Bayesian approach to the triage problem with imperfect classification," European Journal of Operational Research, Elsevier, vol. 215(1), pages 169-180, November.
    • Handle: RePEc:eee:ejores:v:215:y:2011:i:1:p:169-180
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    References listed on IDEAS

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    Cited by:

    1. Avishai Mandelbaum & Petar Momčilović, 2017. "Personalized queues: the customer view, via a fluid model of serving least-patient first," Queueing Systems: Theory and Applications, Springer, vol. 87(1), pages 23-53, October.
    2. Urtzi Ayesta & M Erausquin & E Ferreira & P Jacko, 2016. "Optimal Dynamic Resource Allocation to Prevent Defaults," Working Papers hal-01300681, HAL.
    3. Kamali, Behrooz & Bish, Douglas & Glick, Roger, 2017. "Optimal service order for mass-casualty incident response," European Journal of Operational Research, Elsevier, vol. 261(1), pages 355-367.
    4. Urtzi Ayesta & M Erausquin & E Ferreira & P Jacko, 2016. "Optimal Dynamic Resource Allocation to Prevent Defaults," Post-Print hal-01300681, HAL.
    5. Lee, Hyun-Rok & Lee, Taesik, 2021. "Multi-agent reinforcement learning algorithm to solve a partially-observable multi-agent problem in disaster response," European Journal of Operational Research, Elsevier, vol. 291(1), pages 296-308.
    6. Fernández, Arturo J., 2012. "Minimizing the area of a Pareto confidence region," European Journal of Operational Research, Elsevier, vol. 221(1), pages 205-212.

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