TY - JOUR
T1 - A Bayesian approach to the triage problem with imperfect classification
AU - Li, Dong
AU - Glazebrook, Kevin 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. (C) 2011 Elsevier B.V. All rights reserved.
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. (C) 2011 Elsevier B.V. All rights reserved.
UR - http://www.scopus.com/inward/record.url?scp=79960927420&partnerID=8YFLogxK
U2 - 10.1016/j.ejor.2011.05.044
DO - 10.1016/j.ejor.2011.05.044
M3 - Article
SN - 0377-2217
VL - 215
SP - 169
EP - 180
JO - European Journal of Operational Research
JF - European Journal of Operational Research
IS - 1
ER -