• 산업경영시스템학회지
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• 제40권3호
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• pp.66-75
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• 2017

Priority disciplines are an important scheme for service systems to differentiate their services for different classes of customers. (N, n)-preemptive priority disciplines enable system engineers to fine-tune the performances of different classes of customers arriving to the system. Due to this virtue of controllability, (N, n)-preemptive priority queueing models can be applied to various types of systems in which the service performances of different classes of customers need to be adjusted for a complex objective. In this paper, we extend the existing (N, n)-preemptive resume and (N, n)-preemptive repeat-identical priority queueing models to the (N, n)-preemptive repeat-different priority queueing model. We derive the queue-length distributions in the M/G/1 queueing model with two classes of customers, under the (N, n)-preemptive repeat-different priority discipline. In order to derive the queue-length distributions, we employ an analysis of the effective service time of a low-priority customer, a delay cycle analysis, and a joint transformation method. We then derive the first and second moments of the queue lengths of high- and low-priority customers. We also present a numerical example for the first and second moments of the queue length of high- and low-priority customers. Through doing this, we show that, under the (N, n)-preemptive repeat-different priority discipline, the first and second moments of customers with high priority are bounded by some upper bounds, regardless of the service characteristics of customers with low priority. This property may help system engineers design such service systems that guarantee the mean and variance of delay for primary users under a certain bounds, when preempted services have to be restarted with another service time resampled from the same service time distribution.

• Journal of the Korean Statistical Society
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• 제32권3호
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• pp.311-311
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• 2003

This paper contains the following errors. 1. "$I_{\{x>D\}}{\lambda}dt_{p0}(t)s(x)$" should be added to the right hand side of (2.3). 2. "$I_{\{x>D\}}{\lambda}_{p0}(t)s(x)$" should be added to the right hand side of (2.6). 3. "$I_{\{x>D\}}{\lambda}_{p0}s(x)$" should be added to the right hand side of (2.9). 4. In Theorem 2.3 and its proof, "${\lambda}{\int}_{0}^{D}f(y)s(x-y)dy$" appears three times (including one in (2.20)). To each of these, "${\lambda}_{po}s(x)$" should be added. 5. In Remark 2.5, "${\lambda}dt_{p0}/s(x)dx" should be added to "${\int}_{0}^{D}{\lambda}dt\;s(x-y)dxf(y)dy$". As a result of these corrections, a simpler proof of Theorem 2.3 becomes available. Substituting (2.18), (2.21), (2.22) into the left hand side of (2.20) and comparing the result with (2.10), we have the right hand side of (2.20).

• 한국통신학회논문지
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• 제17권11호
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• pp.1206-1218
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• 1992

토큰으로 매체 이용 자격을 부여하는 토큰링 시스템은 링 혹은 버스 구조의 LAN에서 매체 이용방법으로 흔히 이용되어 왔으며 이러한 시스템으로 모델링 될 수 있다. 이 시스템은 단순히 프레임을 저장하고 전송 조건에 해당되면 전송을 진행하는 브리지를 이용함으로써 단일 링들이 상호 연결된 멀티 토큰링 시스템으로 시스템 규모를 확장할 수 있다. 본 논문에서는 상호 연결된 토큰링 시스템의 전송성능을 분석하기 위하여 스테이션 큐에 도착하는 프레임들이 Poisson과정이고 각 도착 프레임의 길이가 서로 독립이며 동등한 일반 분포를 갖는 M/G/1 큐잉 시스템을 가정하여, 임의의 프레임이 목적지까지 전달에 영향을 미치는 해석적으로 조사하였다. 프레임 전송 지연시간에 영향을 미치는 요소는 임의의 시간으로 부터 토큰이 전송하는 스테이션까지 오는데 걸리는 시간, 전송 스테이션의 전송 시간부터 스테이션 내의 대상 프레임이 전송되기까지 소요되는 대기시간, 대상 프레임의 길이와 목적지까지 소요되는 시간의 합으로 주어지며, 외부링으로부터 내부링 브리지 큐까지. 그리고 외부링 브리지 큐로부터 목적 스테이션 까지의 시간에 대한 합으로 주어진다. 또한 전송지연시간에 미치는 파라미터들을 변화시킬때 전체 지연시간에 미치는 영향을 조사하였다. 그 결과 프레임 전송 지연시간에 상대적으로 큰 영향을 미치는 요소는 프레임의 평균 도착율이 크고 수신 스테이션이 타 외부링일 비율이 커질때이며, 이는 우선순위 서비스 방식의 이용 여부에 관계없이 지연 시간이 지수적 증가를 나타낸다. 이를 감소시키려면 외부링 스테이션의 수를 줄여 상대적으로 중추링의 스테이션 수를 증가시킴으로써 전체 지연시간을 줄일 수 있다.

• 한국통신학회논문지
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• 제32권12A호
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• pp.1320-1328
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• 2007

최근 무선 LAN의 인기로 IEEE 802.11 프로토콜의 성능 분석과 개선에 많은 관심이 생겨났다. 본 논문에서는 도착하는 패킷의 크기가 일반 확률분포를 가질 때 MAC 계층 패킷 서비스 시간을 조사하여 IEEE 802.11 프로토콜의 두 가지 매체 접속 방식을 분석한다. 무선 LAN에서 IEEE 802.11 프로토콜의 수율 및 지연 성능을 분석하기 위해 M/G/1/K 큐잉 모델을 사용한다. 두 가지 접속 방법, 기본 접속과 RTS/CTS 접속 방식의 성능을 비교한다. 그리고 시스템의 수율 및 평균 패킷 지연과 패킷 블록킹 확률을 포함하여 큐의 동작 상태를 보기 위한 여러가지 수치예를 보여준다.

• 대한산업공학회지
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• 제31권4호
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• pp.297-302
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• 2005

Kopzon and Weiss adopted the notion of push-pull into the queueing system recently. We extend this queueing system to the ($N_1,N_2$)-policy version such that the original system corresponds to the special case $N_1=N_2=1$. For the extended system, we perform the cycle analysis and obtain the PGF of the stationary number of customers in the system.

• Asia pacific journal of information systems
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• 제26권3호
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• pp.427-448
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• 2016

The stochastic phenomena of traffic network condition, such as traffic speed and density, are affected not only by exogenous traffic control but also by endogenous changes in service time during congestion. In this paper, we propose a mixed M/G/1 queuing model by introducing a condition-varying parameter of traffic congestion to reflect structural changes in the traffic network. We also develop congestion indices to evaluate network efficiency in terms of traffic flow and economic cost in traffic operating system using structure-changing queuing model, and perform scenario analysis according to various traffic network improvement policies. Empirical analysis using Korean highway traffic operating system shows that our suggested model better captures structural changes in the traffic queue. The scenario analysis also shows that occasional reversible lane operation during peak times can be more efficient and feasible than regular lane extension in Korea.

• 한국신뢰성학회지:신뢰성응용연구
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• 제16권4호
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• pp.313-322
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• 2016

Purpose: We introduce ways to employ Markov chain model to evaluate the effect of preventive maintenance process. While the preventive maintenance process decreases the failure rate of each subsystems, it increases the downtime of the system because the system can not work during the maintenance process. The goal of this paper is to introduce ways to analyze this trade-off. Methods: Markov chain models are employed. We derive the availability of the system consisting of N repairable subsystems by the methods under various maintenance policies. Results: To validate our methods, we apply our models to the real maintenance data reports of military truck. The error between the model and the data was about 1%. Conclusion: The models developed in this paper fit real data well. These techniques can be applied to calculate the availability under various preventive maintenance policies.

• 경영과학
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• 제25권3호
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• pp.87-99
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• 2008

Besieged by needs for upgrading the current Internet, social pressures, and regulatory concerns, a network operator may be left with few options to Improve his services. Yet he can still consider a transition prioritizing network services. In this paper, we describe a transition from a non-priority system to a prioritized one, using non-preemptive M/G/1 model. After reviewing the constraints and theoretical results from past research, we describe steps making the transition Pareto-improving, which boils down to a multi-goal search for a Pareto-improving state. We use a genetic algorithm that captures actual transition costs along with incentive-compatible and Pareto-Improving constraints. Simulation results demonstrate that the initial post-transition solutions are typically Pareto-improving. for non Pareto-improving solutions, the heuristic quickly generates Pareto-improving and incentive-compatible solutions.

• 산업경영시스템학회지
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• 제35권4호
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• pp.162-170
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• 2012

We propose a new priority discipline called the strict T-preemptive priority discipline, and derive the waiting time distributions of each class in the strict T-preemptive priority M/G/1 queue. Using this queueing analysis, we evaluate the performance of an opportunistic spectrum access in cognitive radio networks, where a communication channel is divided into time slots, a licensed primary user is assigned to one channel, and multiple unlicensed secondary users may opportunistically exploit time slots unused by the primary user. We also present a numerical example of the analysis of the opportunistic spectrum access where the arrival rates and service times distributions of each users are identical.

• 대한산업공학회지
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• 제21권2호
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• pp.167-181
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• 1995

We develop an analytical model to estimate the time a workpiece spends in both input and output queues in trip-based material handling systems. The waiting times in the input queues are approximated by M/G/1 queueing system and the waiting times in the output queues are estimated using the method discussed in Bozer, Cho, and Srinivasan [2]. The analytical results are tested via simulation experiment. The result indicates that the analytical model estimates the expected waiting times in both the input and output queues fairly accurately. Furthermore, we observe that a workpiece spends more time waiting for a processor than waiting for a device even if the processors and the devices are equally utilized. It is also noted that the expected waiting time in the output queue with fewer faster devices is shorter than that obtained with multiple slower devices.