CALL CENTERS (CENTRES) - Faculty of Industrial Engineering and ...

standing in the queue during their waiting period. Such circumstances apply, for example, in
telephone centers or other remote service facilities, to which we refer as tele-queues. We analyze
this decision model within a multi-server queue with impatient customers, and seek to characterize
the Nash equilibria of this system. These equilibria may be viewed as stable operating points
of the system, and determine the customer abandonment profile along with other system-wide
performance measures. We provide conditions for the existence and uniqueness of the equilibrium,
and suggest procedures for its computation. We also suggest a notion of an equilibrium
based on sub-optimal decisions, the myopic equilibrium, which enjoys favorable analytical properties.
Some concrete examples are provided to illustrate the modeling approach and analysis.
The present paper supplements previous ones which were restricted to linear waiting costs or
heterogeneous customer population.
Keywords: Tele-queues or invisible queues, Abandonment, Impatient customers, Nash equilibrium,
Telephone call centers, Contact centers, Multi-server queues
141. Shumsky, Robert A. Approximation and analysis of a call center with flexible and specialized
servers, OR Spectrum, 26 (3), 2004, 307–330.
Abstract. This paper describes a decomposition algorithm to estimate the performance of a
call center with two types of customers and two server categories. In this system, specialized
servers can process only one customer type, while flexible servers handle both types. The algorithm
divides the systems state space into regions, and simple approximate models find the
conditional system performance within each region. While the procedure described here is tailored
for a system with a priority queue discipline and two customer classes, it can be adapted
for systems with FCFS queue disciplines and for systems with more than two customer types.
Performance measures generated by the procedure are sufficiently accurate for many service system
design decisions, such as setting telephone call center staffing levels and long-term capacity
planning. The procedure is also extremely fast, and its computational requirements do not grow
with system congestion. Numerical tests demonstrate that its running time is significantly lower
than traditional numerical methods for generating approximations. As an example of its use, we
employ the procedure to demonstrate the benefits of server flexibility in a particular telephone
call center.
Keywords: Servers, Call centers, Studies, Algorithms, Queueing
142. Sisselman, Michael E. and Ward Whitt. Empowering customer-contact agents via preferencebased
routing. SeatLink Working paper, 2004. Available at:
.
Abstract. SeatLink improves the overall performance of a contact center, by allowing agents
to influence the routing of inbound interactions based on their personal preferences.
143. Steckley, Samuel G., Shane G. Henderson and Vijay Mehrotra. Service system planning in the
presence of a random arrival rate. Working paper, School of Operations Research and Industrial
Engineering, Cornell University, November 1, 2004.
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