A Course on Queueing Models

Jain, J. L.; Mohanty, Gopal; Böhm, Walter

doi:10.1201/b15892

Cited by 22 publications

(19 citation statements)

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“…Previous researchers have examined queuing process extensively and developed analytical models used for examining queue performance [3,4,16,20,24,25]. The random arrivals described by Poisson distribution are expressed such that, for number of arrivals N(t) within time interval [0, t] with arrival rate , the expression of time between successive arrivals is exponentially distributed with parameter and independent of previous arrivals.…”

Section: Analytical Models For Queuing Studiesmentioning

confidence: 99%

“…When < or m > such that < 1 for Eq. (10) and (11) respectively, the system is classified as a steady state or an equilibrium system; otherwise, unsteady or nonequilibrium where arrivals continue to increase indefinitely [3,16]. Demand intensity of the system at each arrival can be estimated using Eq.…”

Section: Analytical Models For Queuing Studiesmentioning

confidence: 99%

“…Whenever > 1, the system is said to be overloaded, that is, random arrivals are relatively higher than departures, hence system is unsteady [3,18]. (11); where, is the number of servers in the system.…”

Section: Analytical Models For Queuing Studiesmentioning

confidence: 99%

“…Service times are independently and identically distributed or exponentially distributed random variables; the process is expressed by Erlange model [17, 19 -21]. Queuing models are described using probability distribution of inter-arrivals and service times, number of servers and queue discipline forming the Erlangian models [3,22]. Queuing models are categorised into deterministic, where inter-arrival times and service times are constant or Markovian where inter-arrival time and service times are exponentially distributed or independently and identically distributed (IID).…”

Section: Introductionmentioning

confidence: 99%

“…The concept of queuing is generally employed by waiting customers in service delivery systems to ensure orderliness and efficient operations of the system [1][2][3]. The concept was first used for the analysis of telephone call traffic in 1913 [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Modelling of queuing process at airport check-in system: a case study of Manchester and Leeds-Bradford airports

Adeke

2018

Nig. J. Tech.

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This study built a Simulation Model (SM) using SimEvents toolbox in MATLAB for implementing Analytical Models (AM) of queuing process at airport check-in system. Air travel demand data for Manchester and Leeds-Bradford airports in 2014 were adopted for validation of the model. There was no statistical difference between utilisation factor (UF) and service times of AM and SM outputs. Differences in AM and SM outputs for average queue length, average waiting time on queue and average number of arrivals and throughputs were attributed to variations in discrete time events considered by SM in contrary to the AM which assumed constant values for the process. The SM exhibited stochastic behaviour which actually depicts reality hence produces more reliable results. Stochastic analysis methods are therefore recommended for queuing analysis to achieve accurate results. The SM is therefore recommended to give Airport managers prior knowledge of system performance for planning and improved level of service (LOS) at airports.

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Section: Analytical Models For Queuing Studiesmentioning

confidence: 99%

Section: Analytical Models For Queuing Studiesmentioning

confidence: 99%

Section: Analytical Models For Queuing Studiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Modelling of queuing process at airport check-in system: a case study of Manchester and Leeds-Bradford airports

Adeke

2018

Nig. J. Tech.

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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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Exclusive Queueing Process with Discrete Time

Arita

Yanagisawa

2010

J Stat Phys

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In a recent study [C Arita, Phys. Rev. E 80, 051119 (2009)], an extension of the M/M/1 queueing process with the excluded-volume effect as in the totally asymmetric simple exclusion process (TASEP) was introduced. In this paper, we consider its discrete-time version. The update scheme we take is the parallel one. A stationary-state solution is obtained in a slightly arranged matrix product form of the discrete-time open TASEP with the parallel update. We find the phase diagram for the existence of the stationary state. The critical line which separates the parameter space into the regions with and without the stationary state can be written in terms of the stationary current of the open TASEP. We calculate the average length of the system and the average number of particles

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A Course on Queueing Models

Cited by 22 publications

References 0 publications

Modelling of queuing process at airport check-in system: a case study of Manchester and Leeds-Bradford airports

Modelling of queuing process at airport check-in system: a case study of Manchester and Leeds-Bradford airports

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

Exclusive Queueing Process with Discrete Time

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