Many-server queues are building blocks to model many large-scale service systems such as call centers and hospitals. In such a system, customer abandonment can have a significant impact on revenue and system performance. When a customer's waiting time in queue exceeds his patience time, he abandons the system without service. We assume that customer service and patience times form two sequences of independent, identically distributed (iid) nonnegative random variables, having general distributions. Recent call center and hospital data show that these distributions are not exponential, despite most of the research to date assumes that at least one of these two distributions is exponential. I will discuss the sensitivity of service and patience time distributions for queues in many-server heavy traffic, and its implications on model identification, numerical algorithms, and asymptotic analysis.
This is joint work with Shuangchi He at Georgia Tech and Tolga Tezcan, who is currently at University of Illinois and is going to join Rochester University in fall 2010.