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

95. Puhalskii, A.A. and M.I. Reiman. The multiclass GI/PH/N queue in the Halfin-Whitt regime,
Advances in Applied Probability, 32 (2), 2000, 564–595.
Abstract. A consideration is made of a multiserver queue in the heavy-traffic regime introduced
and studied by Halfin and Whitt (1981) who investigated the case of the single customer class
with exponentially distributed server times. The purpose is to extend their analysis to a system
with multiple customer classes, priorities and phase-type service distributions. A weak convergence
limit theorem is proven showing that a properly defined and normalized queue length
process converges to a particular K-dimensional diffusion process, where K is the number of
phases in the service time distribution. It is also shown that a properly normalized waiting time
process converges to a simple functional of the limit diffusion for the queue length.
Keywords: Call Centers, Multiserver queues, Priority queues, Heavy traffic, Diffusion approximation,
Weak convergence
96. Reiman, Martin I. Diffusion limits for multiskill call centers with many agents. Applied Probability
Society at INFORMS 2000, San Antonio, Nov. 5–8, 2000.
Abstract. We consider a queueing model of a call center providing service to several customer
types (skills), where each server (agent) can handle some subset of the skills. We examine this
model in the Halfin-Whitt regime, which involves the number of servers growing large while the
traffic intensity approaches unity.
97. Ridley, A. Performance optimization of a telecommunication call center. Proceedings of the
Applied Telecommunication Symposium (ATS’00). SCS, San Diego, CA, USA, 2000, 163–167.
Abstract. Telecommunication call centers have become the primary channel of customer interaction
service for many businesses. The level of professionalism and efficiency that call center
agents deliver to customers provides a significant advantage over traditional customer service
practices. The growth of call centers has been substantial over the last two decades. This growth
is driven by a company’s desire to lower operating costs and to increase revenues (Kim 1997).
The author investigates analytical and simulation-based models for the design and management
of a call center. Given three classes of traffic (voice, E-mail, and facsimile) with different
target waiting-times in queue and target service levels, the goal is to optimize the call center
performance. The system performance can be measured with quantities such as the expected
waiting-time in queue, the expected time in system, the percentage of calls answered within a
given time, and the expected waiting-time probability distribution. The system performance of
the call center is measured using analytical and simulation-based queuing models. For analytical
models, the traffic classes will have exponential inter-arrival and service time distributions where
the arrival and service rates will differ among classes. Also, each customer call will be assigned
a queue priority based on its traffic class. The call agents will be able to handle calls from any
class. For the simulation-based models, the inter-arrival and service time distributions will not
be exponential, the agents will have different skill-levels, and the queue length will be finite.
Keywords: Performance optimization, Telecommunication call center, Simulation-based models,
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