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Department of Statistical and Actuarial Sciences
Center of Actuarial Excellence (CAE)
2014 Science Dean Homecoming Celebration
2014 Science Dean Homecoming Celebration

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  • Colloquium (Thursday, January 29, 2015)
    Time: 3:30pm-4:30pm and Room: WSC 248
    Speaker: Dr. Dave Stanford
    Title: A New Patient Selection Strategy to Minimize Expected Excess Waiting Time

    David Stanford, Statistical & Actuarial Sciences, UWO (co-authors Azaz Sharif, UWO, and Rick Caron, U. Windsor)

    For many decades, the dominant two strategies in terms of queueing disciplines to select patients for treatment have been the first-come, first-served (FCFS) discipline and priority disciplines that separate patients by acuity or some other indicator. The problem with the former is that various patient groups may have differing urgencies, while the problem with the latter is that some patients may be repeatedly overtaken by those of greater urgency, leading to very long waiting times. The length of the experienced wait is not reflected in who goes next.

    Recently, Stanford et al (2014) revisited a model proposed initially by Kleinrock in which patients accumulate priority credit over time (now called the Accumulating Priority Queue). They determined the waiting-time distributions for each class of patients. This model is ideally suited for healthcare and other service systems in which various service standards for the incurred waiting time need to be met.

    Key Performance Indicators (KPIs) are one measure of service system performance which comprise a delay limit and compliance probability (the chance a customer starts service by the limit). While our motivating KPIs arise in the field of health care, other applications arise in call centre settings, telecommunication messaging systems, and elsewhere. The primary flaw of a pure KPI approach is that no consideration is given for the consequences of customers whose waiting time exceeds the delay limit.
    We present an optimization model for such systems which seeks to minimize the weighted average of expected excess waiting time for the various classes. We test the model extensiv ...

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