Majakwara, Jacob (2009) Application of multiserver queueing to call centres. Masters thesis, Rhodes University.

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Thesis_Mjakwara.pdf 1102Kb 
Abstract
The simplest and most widely used queueing model in call centres is the MMk system, sometimes referred to as ErlangC. For many applications the model is an oversimplification. ErlangC model ignores among other things busy signals, customer impatience and services that span multiple visits. Although the ErlangC formula is easily implemented, it is not easy to obtain insight from its answers (for example, to find an approximate answer to questions such as “how many additional agents do I need if the arrival rate doubles?”). An approximation of the ErlangC formula that gives structural insight into this type of question would be of use to better understand economies of scale in call centre operations. ErlangC based predictions can also turn out highly inaccurate because of violations of underlying assumptions and these violations are not straightforward to model. For example, nonexponential service times lead one to the MGk queue which, in stark contrast to the MMk system, is difficult to analyse. This thesis deals mainly with the general MGIk model with abandonment. The arrival process conforms to a Poisson process, service durations are independent and identically distributed with a general distribution, there are k servers, and independent and identically distributed customer abandoning times with a general distribution. This thesis will endeavour to analyse call centres using MGIk model with abandonment and the data to be used will be simulated using EZSIMsoftware. The paper by Brown et al [3] entitled “Statistical Analysis of a Telephone Call Centre: A QueueingScience Perspective,” will be the basis upon which this thesis is built.
Item Type:  Thesis (Masters) 

Uncontrolled Keywords:  Call centers, ERLANG (Computer program language), Queuing theory 
Subjects:  Q Science > QA Mathematics > QA273 Probabilities. Mathematical statistics 
Divisions:  Faculty > Faculty of Commerce > Statistics Faculty > Faculty of Science > Statistics 
Supervisors:  Szyszkowski, I 
ID Code:  1889 
Deposited By:  Mrs Carol Perold 
Deposited On:  09 Feb 2011 09:08 
Last Modified:  06 Jan 2012 16:21 
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