A discrete-time retrial queueing model with one server

Nobel, R.D.; Moreno, Pilar

doi:10.1016/j.ejor.2007.03.051

Cited by 9 publications

(17 citation statements)

References 16 publications

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“…Notice that the 'plus 1' is required because in our setup the server is always idle for at least one time slot immediately after the departure of a customer. Nobel and Moreno (2008) contains the formal proof of this ergodicity condition.…”

Section: The Standard Lars-da Retrial Modelmentioning

confidence: 99%

“…In this section we present the precise description of the discrete-time one-server retrial queueing model with the late arrival setup and delayed access [the LARS-DA model, see Nobel and Moreno (2008) for more details]. In each time slot customers arrive in batches.…”

Section: The Standard Lars-da Retrial Modelmentioning

confidence: 99%

“…So even when a customer finds the server idle upon arrival his service will start only in the next slot at the earliest. In this paper we only discuss the steady-state behavior of the number of customers in the orbit [for other performance measures we refer again to Nobel and Moreno (2008)]. …”

Section: The Standard Lars-da Retrial Modelmentioning

confidence: 99%

“…Not surprisingly in all these papers the EAS setup is chosen and we will not discuss these papers any further in this survey. For a comparison between the EAS-setup and the LAS-setup we refer to Nobel and Moreno (2008) where the differences are analyzed in detail. As said before we prefer the LAS-setup, and therefore we will take the standard discrete-time one-server delay model of Bruneel and Kim (1993) as our starting point and we will analyze several retrial variants of that model.…”

Section: Introductionmentioning

confidence: 99%

“…As said before we prefer the LAS-setup, and therefore we will take the standard discrete-time one-server delay model of Bruneel and Kim (1993) as our starting point and we will analyze several retrial variants of that model. So, to resume the setup very briefly [see Nobel and Moreno (2008) for more details], customers arrive in batches during a slot and the batch size follows a general probability distribution. Customers who upon arrival find the server busy are sent into the orbit.…”

Section: Introductionmentioning

confidence: 99%

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Retrial queueing models in discrete time: a short survey of some late arrival models

Nobel

2015

Ann Oper Res

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This paper presents an overview of one-server queueing models with retrials in discrete-time. In all these models the number of primary customers arriving in a time slot follows a general probability distribution and the different numbers of primary arrivals in consecutive time slots are mutually independent. Each customer requires from the server a generally distributed number of slots for his service, and the service times of the different customers are independent. Only models with delayed access are considered, and the socalled late arrival setup is chosen. For all the models the steady-state behavior is studied through the generating function of the number of customers in the orbit. From the generating function several performance measures are deduced, like the average orbit size and the mean busy period.

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Section: The Standard Lars-da Retrial Modelmentioning

confidence: 99%

Section: The Standard Lars-da Retrial Modelmentioning

confidence: 99%

Section: The Standard Lars-da Retrial Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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