Xu Sun, ISE Faculty Search Candidate Seminar

Date/Time

09/18/2018
11:45 am-12:35 pm
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Location

CSE Room E-107
UF Campus
Gainesville, FL 32611

Details

Xu Sun, Ph.D. Candidate
Columbia University

Title: Staffing and Scheduling to Differentiate Service in Multi-class Time-Varying Service Systems

Abstract: Queueing theory is a field driven by applications. But unfortunately, there still remains a large gap between tractable theoretical studies and practical applications, such as call centers and health care systems, which have many realistic features (e.g., time-varying arrivals, customer abandonment, general service-time distributions, and complicated network structures). In response to these challenges, we study a practical queueing system having multiple customer classes, nonstationary customer arrivals, and customer abandonment. We will develop effective staffing rules (number of servers) and scheduling policies (assigning newly idle servers to a waiting customer from one of the classes), with the objective of achieving differentiated service levels for each customer class.
One notable motivation of this research is the Canadian triage and acuity scale (CTAS) guideline that classifies patients in the emergency department (ED) into five acuity levels. In particular, CTAS requires that “level i patients need to be seen by a physician within w_i minutes 100α_i% of the time”, with (w_1, w_2, w_3, w_4, w_5) = (0, 15, 30, 60, 120) minutes, and (α_1, α_2,α_3, α_4, α_5) = (0.98, 0.95, 0.9, 0.85, 0.8). Our goal is to devise new control principles (via staffing and scheduling) to guarantee that P(〖〖W_i (t)>w〗_i)≤α〗_i, that is, the probability that a class-i customer waits more than w_i does not exceed α_i at all times for all classes. Our new joint staffing and scheduling policy is both time dependent (which copes with the time variability in arrival pattern) and state dependent (which dynamically captures the stochastic variability in service times and arrival times). Effectiveness of our policy is substantiated by asymptotic optimality theorems. We also conduct computer simulation experiments to provide engineering confirmation and to gain insights.

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Department of Industrial and Systems Engineering at the University of Florida