Seminar

Abstract:
We study call center operations management using stochastic modeling.  We focus on two projects, one on delay information, and the other on customer routing.

In the first project, we analyze a call center with impatient customers and study how informing customers about their anticipated delays affects performance. Customers react by balking upon hearing the delay announcement, and may subsequently abandon, particularly if the realized waiting time exceeds the delay that has originally been announced to them. The balking and abandonment are a function of the delay announcement. Modeling the call center as a queue with endogenized customer reactions to announcements, we analytically characterize performance measures for this model. The analysis allows us to explore the role announcing different percentiles of the waiting time distribution, i.e., announcement coverage, plays on subsequent performance in terms of balking and abandonment. We explore when informing customers about delays is beneficial, and what the optimal coverage should be in these announcements.

In the second project, we study call rejection and agent reservation strategies in multi-channel call centers with inbound and outbound calls. The following questions are addressed: How should the rejection of inbound calls from the queue be done and when should agents initiate outbound calls? The firm is looking for the best possible trade-off between the throughput of outbound calls and the service levels of inbound calls. We tackle these questions by characterizing the optimal scheduling policies. Two classes of policies for inbound calls rejection are considered: Rejection upon arrival so-called a priori, and rejection after experimenting some wait so-called rejection a posteriori. This class of policies has not been considered in the literature so far. We show for example that rejection a posteriori outperforms rejection a priori when considering served customers. The opposite is true when considering the expected unconditional waiting time in the queue.