Scheduling impatient jobs in a clearing system with insights on patient triage in mass casualty incidents

  • Authors:

  • Nilay tanik Argon;Serhan Ziya;Rhonda Righter

  • Affiliations:

  • Department of statistics and operations researchuniversity of north carolinachapel hill, nc 27599-3180 e-mail: nilay@email.unc.edu/ ziya@email.unc.edu;Department of statistics and operations researchuniversity of north carolinachapel hill, nc 27599-3180 e-mail: nilay@email.unc.edu/ ziya@email.unc.edu;Department of industrial engineering and operations researchuniversity of californiaberkeley, ca 94720-1777 e-mail: rrighter@ieor.berkeley.edu

  • Venue:
  • Probability in the Engineering and Informational Sciences
  • Year:
  • 2008

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Abstract

Motivated by the patient triage problem in emergency response, we consider a single-server clearing system in which jobs might abandon the system if they are not taken into service within their “lifetime.” In this system, jobs are characterized by their lifetime and service time distributions. Our objective is to dynamically determine the optimal or near-optimal order of service for jobs so as to minimize the total number of abandonments. We first show that if the jobs can be ordered in such a way that the job with the shortest lifetime (in the sense of hazard rate ordering) also has the shortest service time (in the sense of likelihood ratio ordering), then the optimal policy gives the highest priority to this “time-critical” job independently of the system state. For the case in which the jobs with shorter lifetimes have longer service times, we observed that the optimal policy generally has a complex structure that might depend on the type and number of jobs available. For this case, we provide partial characterizations of the optimal policy and obtain sufficient conditions under which a state-independent policy is optimal. Furthermore, we develop two state-dependent heuristic policies, and by means of a numerical study, we show that these heuristics perform well, especially when jobs abandon the system at a relatively faster rate when compared to service rates. Based on our analytical and numerical results, we develop several insights on patient triage in the immediate aftermath of a mass casualty event. For example, we conclude that in a worst-case scenario, where medical resources are overwhelmed with a large number of casualties who need immediate attention, it is crucial to implement state-dependent policies such as the heuristic policies proposed in this article.