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Journal of the Chungcheng Mathematical Society
Article . 2016 . Peer-reviewed
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AN APPROXIMATION FOR THE QUEUE LENGTH DISTRIBUTION IN A MULTI-SERVER RETRIAL QUEUE

Authors: Jeongsim Kim;

AN APPROXIMATION FOR THE QUEUE LENGTH DISTRIBUTION IN A MULTI-SERVER RETRIAL QUEUE

Abstract

Abstract. Multi-server queueing systems with retrials are widelyused to model problems in a call center. We present an explicitformula for an approximation of the queue length distribution in amulti-server retrial queue, by using the Lerch transcendent. Accu-racy of our approximation is shown in the numerical examples. 1. IntroductionRetrial queues are queueing systems in which arriving customerswho find all servers occupied may retry for service again after a ran-dom amount of time. Retrial queues have been widely used to modelmany problems/situations in telephone systems, call centers, telecom-munication networks, computer networks and computer systems, and indaily life. For an overview regarding retrial queues, refer to the sur-veys [9, 13, 14]. For further details, refer to the books [7, 10] and thebibliographies [3, 4, 5].Typically a call center consists of a finite number of servers that an-swer customer’s calls, and it can be modelled as a queueing system. In aqueueing model of a call center, the customers are callers and the serversare either telephone operators or communication equipment. Queues areformed by callers who are waiting service. The call center can be de-scribed as follows: When a customer’s call arrives, it will be servedimmediately if a server is available. However, if all servers are busy at

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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