• Journal of applied mathematics & informatics
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• v.5 no.2
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• pp.403-426
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• 1998

We present an algorithm to find an approximation for the stationary distribution for the general ergodic spatially-inhomogeneous block-partitioned upper Hessenberg form. Our approximation makes use of an associated upper block-Hessenberg matrix which is spa-tially homogeneous except for a finite number of blocks. We treat the MAP/G/1 retrial queue and the retrial queue with two types of customer as specific instances and give some numerical examples. The numerical results suggest that our method is superior to the ordinary finite-truncation method.

• Journal of applied mathematics & informatics
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• v.25 no.1_2
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• pp.389-405
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• 2007

In this paper, we study a k-out-of-n system with single server who provides service to external customers also. The system consists of two parts:(i) a main queue consisting of customers (failed components of the k-out-of-n system) and (ii) a pool (of finite capacity M) of external customers together with an orbit for external customers who find the pool full. An external customer who finds the pool full on arrival, joins the orbit with probability ${\gamma}$ and with probability $1-{\gamma}$ leaves the system forever. An orbital customer, who finds the pool full, at an epoch of repeated attempt, returns to orbit with probability ${\delta}\;(<\;1)$ and with probability $1-{\delta}$ leaves the system forever. We compute the steady state system size probability. Several performance measures are computed, numerical illustrations are provided.

• Journal of Korean Institute of Industrial Engineers
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• v.25 no.1
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• pp.125-129
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• 1999

The Little's formula, $L={\lambda}W$, expresses a fundamental principle of queueing theory: Under very general conditions, the average queue length is equal to the product of the arrival rate and the average waiting time. This useful formula is now well known and frequently applied. In this paper, we demonstrate that the Little's formula has much more power than was previously realized when it is properly decomposed into what we call the microscopic Little's formula. We use the M/G/1 queue with server vacations as an example model to which we apply the microscopic Little's formula. As a result, we obtain a transform-free expression for the queue length distribution. Also, we briefly summarize some previous efforts in the literature to increase the power of the Little's formula.

• Journal of Korean Institute of Industrial Engineers
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• v.24 no.3
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• pp.381-386
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• 1998

We frequently encounter stopped random sums when modelling queueing systems. We also notice occasional mishandling of stopped random sums in the literature. The purpose of this note is to prevent further mistakes by identifying and correcting typical mistakes about stopped random sums. As an example model, we use the two-phase M/G/1 queue with multiple vacations.

• Journal of Korean Institute of Industrial Engineers
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• v.33 no.4
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• pp.419-423
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• 2007

In this paper, we consider the M/G/1 queueing system with randomized control of T-policy. Whenever the busy period ends, the server is turned off and takes multiple vacations whose interval is fixed time T with probability p or stays on and waits for arriving customers with probability 1-p. We introduce the cost function and determine the optimal combination of (p, T) to minimize the average cost per unit time.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2002.05a
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• pp.841-844
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• 2002

Recently there has been an increasing interest in queueing models with disasters. Upon arrival of a disaster, all the customers present are noshed out. Queueing models with disasters have been applied to the problems of failure recovery in many computer networks systems, database systems and telecommunication networks in this paper, we suffest the steady state and sojourn time distributions of the M/G/l model with disaster and mass alway when the system is empty.

• Journal of Applied Reliability
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• v.16 no.4
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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.

• Journal of Internet Computing and Services
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• v.23 no.5
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• pp.33-46
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• 2022

This paper, in order to minimize the ground waiting time of an Air tanker, the queuing theory, that is, a queue that calculates the waiting time under single-server and multi-server situations, was used in the study. Since the national budget and resources are limited, the unlimited increase of the logistics support service team is limited. Therefore, the number of logistic support service teams that can adaptively control the ground waiting time according to the wartime preparation stage or war environment was calculated. The results of this study provide a stipulated standard for calculating the optimal number of air tanker logistic support service teams of the Air Force, providing a basis for the logistical commander to assign logistic support service teams to each stage from peacetime to wartime.