Staffing and Scheduling to Differentiate Service in Many-Server Service Systems
By: Xu Sun
Publisher: [publisher not identified]
Published: 2019
Place of publication: [New York, N.Y.?]
Language: eng
Description: This dissertation contributes to the study of a queueing system with a single pool of multiple homogeneous servers to which multiple classes of customers arrive in independent streams. The objective is to devise appropriate staffing and scheduling policies to achieve specified class-dependent service levels expressed in terms of tail probability of delays. Here staffing and scheduling are concerned with specifying a time-varying number of servers and assigning newly idle servers to a waiting customer from one of K classes, respectively. For this purpose, we propose new staffing-and-scheduling solutions under the critically-loaded and overloaded regimes. In both cases, the proposed solutions are both time dependent (coping with the time variability in the arrival pattern) and state dependent (capturing the stochastic variability in service and arrival times). We prove heavy-traffic limit theorems to substantiate the effectiveness of our proposed staffing and scheduling policies. We also conduct computer simulation experiments to provide engineering confirmation and practical insight.
Notes: Department: Industrial Engineering and Operations Research.

Thesis advisor: Ward Whitt.

Thesis (Ph.D.)--Columbia University, 2019.