• Journal of applied mathematics & informatics
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• v.24 no.1_2
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• pp.529-534
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• 2007

We obtain an explicit expression of the loss probability for the PR/M/1/K queue when the offered load is strictly less than one.

• Journal of Communications and Networks
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• v.17 no.3
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• pp.265-266
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• 2015

In a recent paper [1], the authors investigated the maximum stable throughput region of a network composed of a rechargeable primary user and a secondary user plugged to a reliable power supply. The authors studied the cases of an infinite and a finite energy queue at the primary transmitter. However, the results of the finite case are incorrect. We show that under the proposed energy queue model (a decoupled M/D/1 queueing system with Bernoulli arrivals and the consumption of one energy packet per time slot), the energy queue capacity does not affect the stability region of the network.

• Journal of applied mathematics & informatics
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• v.7 no.1
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• pp.83-100
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• 2000

Many queueing systems such as M/M/s/K retrial queue with impatient customers, MAP/PH/1 retrial queue, retrial queue with two types of customers and MAP/M/$\infty$ queue can be modeled by a level dependent quasi-birth-death(LDQBD) process with linear transition rates of the form ${\lambda}_k$={\alpga}{+}{\beta}k$ at each level $\kappa$. The purpose of this paper is to propose an algorithm to find transient distributions for LDQBD processes with linear transition rates based on the adaptive uniformization technique introduced by van Moorsel and Sanders [11]. We apply the algorithm to some retrial queues and present numerical results.

• Journal of applied mathematics & informatics
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• v.5 no.3
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• pp.827-837
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• 1998

We consider the M/G/1 queue with instantaneous feed-back. The probabilities of feedback are determined by the state of the underlaying Markov chain. by using the supplementary variable method we derive the generating function of the number of customers in the system. In the analysis it is required to calculate the matrix equations. To solve the matrix equations we use the notion of Ex-tended Laplace Transform.

• Journal of the Korea Society for Simulation
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• v.24 no.3
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• pp.27-35
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• 2015

We analyze an M/G/1/K queueing system with queue-length dependent service and arrival rates. There are a single server and a buffer with finite capacity K including a customer in service. The customers are served by a first-come-first-service basis. We put two thresholds $L_1$ and $L_2$($${\geq_-}L_1$$ ) on the buffer. If the queue length at the service initiation epoch is less than the threshold $L_1$, the service time of customers follows $S_1$ with a mean of ${\mu}_1$ and the arrival of customers follows a Poisson process with a rate of ${\lambda}_1$. When the queue length at the service initiation epoch is equal to or greater than $L_1$ and less than $L_2$, the service time is changed to $S_2$ with a mean of $${\mu}_2{\geq_-}{\mu}_1$$. The arrival rate is still ${\lambda}_1$. Finally, if the queue length at the service initiation epoch is greater than $L_2$, the arrival rate of customers are also changed to a value of $${\lambda}_2({\leq_-}{\lambda}_1)$$ and the mean of the service times is ${\mu}_2$. By using the embedded Markov chain method, we derive queue length distribution at departure epochs. We also obtain the queue length distribution at an arbitrary time by the supplementary variable method. Finally, performance measures such as loss probability and mean waiting time are presented.

• Journal of the Korean Operations Research and Management Science Society
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• v.32 no.2
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• pp.141-147
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• 2007

We consider a GI/M/1 queue with vacations such that the server works with different rate rather than completely stops working during a vacation period. We derive the transform of the joint distribution of the length of a busy period, the number of customers served during the busy period, and the length of the subsequent idle period.

• Journal of Korean Institute of Industrial Engineers
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• v.26 no.3
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• pp.283-288
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• 2000

We present several alternative expressions for the decomposition property of the M/G/1 queue with generalized vacations so that a user can choose the most convenient expression for his/her own purpose.

• Journal of the Korean Operations Research and Management Science Society
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• v.27 no.2
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• pp.51-61
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• 2002

By using the arrival time approach of Chae et at. [6], we derive various performance measures including the queue length distributions (in PGFs) and the waiting time distributions (in LST and PGF) for both M$^{x}$ /G/1 and Geo$^{x}$ /G/1 queueing systems, both under the assumption that the server, when it becomes idle, takes multiple vacations up to a random maximum number. This is an extension of both Choudhury[7] and Zhang and Tian [11]. A few mistakes in Zhang and Tian are corrected and meaningful interpretations are supplemented.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.12 no.6
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• pp.545-553
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• 1987

An approximation algorithm has been obtained to analyze the network of single server tandem queues with a finite length. In the queueing network with a finite queue length, the blocking which is mutually dependent, occure due to the limitation of the queue length. Thus, it is difficults to analyze such a queueing network. In this paper each queue has been regarded as the independent M/M/1/K system to analyze the queueing network with the blocking, which is based on the assumption that an arrival rate to the present station is increased by the blocking of the following stations. The performance measures, such as state probability, average queue length and tha waiting time, can be easily obtained using the proposed algorithm. In order to justify this approximation algorithm, comparison of the results of this algorithm with those of state transition simultaneous equations has been made an verified with computer simulation.

• Proceedings of the Korean Statistical Society Conference
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• 2004.11a
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• pp.295-300
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• 2004

We consider a G/M/1 queue with two-stage service policy. The server starts to serve with rate of ${\mu}1$ customers per unit time until the number of customers in the system reaches A. At this moment, the service rate is changed to that of ${\mu}2$ customers per unit time and this rate continues until the system is empty. We obtain the stationary distribution of the number of customers in the system.