• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.4
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• pp.91-105
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• 2019

Discrete-time Queueing models are frequently utilized to analyze the performance of computing and communication systems. The length of busy period is one of important performance measures for such systems. In this paper, we consider the busy period of the Geo/Geo/1/K queue with a single vacation. We derive the moments of the length of the busy (idle) period, the number of customers who arrive and enter the system during the busy (idle) period and the number of customers who arrive but are lost due to no vacancies in the system for both early arrival system (EAS) and late arrival system (LAS). In order to do this, recursive equations for the joint probability generating function of the busy period of the Geo/Geo/1/K queue starting with n, 1 ≤ n ≤ K, customers, the number of customers who arrive and enter the system, and arrive but are lost during that busy period are constructed. Using the result of the busy period analysis, we also numerically study differences of various performance measures between EAS and LAS. This numerical study shows that the performance gap between EAS and LAS increases as the system capacity K decrease, and the arrival rate (probability) approaches the service rate (probability). This performance gap also decreases as the vacation rate (probability) decrease, but it does not shrink to zero.

• Communications for Statistical Applications and Methods
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• v.7 no.2
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• pp.489-497
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• 2000

The M/M/1 queue with impatient customers is studied. Impatient customers wait for service only for limited time K/0 and leave the system if their services do not start during that time. Notice that in the analysis of virtual waiting time, the impatient customer can be considered as the customer who enters the system only when his/her waiting time does not exceed K. In this paper, we apply martingale methods to the virtual waiting time and obtain the expected period from origin to the point where the virtual waiting time crosses over K or reaches 0, and the variance of this period. With this results, we obtain the expected busy period of the queue, the distribution, expectation and variance of the number of times the virtual waiting time exceeding level K during a busy period, and the probability of there being no impatient customers in a busy period.

• Journal of Families and Better Life
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• v.8 no.2
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• pp.163-180
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• 1990

The objective of this study was to investigate the time used for daily activities by rural housewives. This study was carried out two aspects-the amount and the distribution of time. However, the characteristics of agriculture and the farming season influenced on rural housewives activities. There fore, in this study, survey areas divided into two groups-the traditional and the commercial agricultural area. And I conducted surveys in two times-the busy farming season and the leisure season for farmers. Data for 286 housewives(76 in traditional area on the leisure season, and 68 in commercial 142 in traditional area on the busy farming season)were collected by interviews, in which wives were asked to recall the used of time on the previous day, and a time record chart broken into fifteen minute intervals. The statistics for data analysis were frequency, percentile, T-test, and F-test by SPSS PC programs. The findings are as follows; 1)The average total time of rural housewives on labour was 8 hours 53 minutes, on socio-cultural activities 4 hours 18 minutes, and on physiological activities 11 hours 2 minutes. 2) The amount of time on agricultural labour was 6 hours 47 minutes in busy farming season, and 2 hour 45 minutes in leisure season. 3) The average time on household labour was 3 hours 51 minutes. 4) The amount of time on socioculture activities was 2 hours 19 minutes in busy farming, and 6 hours 16 minutes in leisure season. 5) The average time on physiological activities was 11 hours 2 minutes.

• Journal of the Chungcheong Mathematical Society
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• v.25 no.4
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• pp.747-752
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• 2012

When the service time distribution has a finite exponential moment, we present a large deviations result for the busy period distribution in the M/G/1 queue without the assumption of Abate and Whitt (1997) and Kyprianou (1971).

• Journal of the Korean Operations Research and Management Science Society
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• v.31 no.1
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• pp.83-90
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• 2006

By defining the partial busy period of the M/M/c/K queueing system as the time interval during which at least one server is in service, we derive the first two moments of both the partial busy period and the number of customers served during it. All expressions are given in explicit forms.

• Journal of the Korean Operations Research and Management Science Society
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• v.17 no.2
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• pp.123-130
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• 1992

The model of k-out-of n : G repairable system with identical components is extended to a repairable system with n different components. The objective is to analytically derive the mean time of the busy period for a k-out-of-n : G system with unrestricted repair. Then, the lower and upper bounds on the average time of the busy period of the n-component system with restricted repair are also shown.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.38 no.1
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• pp.21-29
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• 2015

A steady-state controllable M/G/1 queueing model operating under the {T:Min(T,N)} policy is considered where the {T:Min(T,N)} policy is defined as the next busy period will be initiated either after T time units elapsed from the end of the previous busy period if at least one customer arrives at the system during that time period, or after T time units elapsed without a customer' arrival, the time instant when Nth customer arrives at the system or T time units elapsed with at least one customer arrives at the system whichever comes first. After deriving the necessary system characteristics including the expected number of customers in the system, the expected length of busy period and so on, the total expected cost function per unit time for the system operation is constructed to determine the optimal operating policy. To do so, the cost elements associated with such system characteristics including the customers' waiting cost in the system and the server's removal and activating cost are defined. Then, procedures to determine the optimal values of the decision variables included in the operating policy are provided based on minimizing the total expected cost function per unit time to operate the queueing system under considerations.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.1
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• pp.81-90
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• 2016

Different from general operating policies to be applied for controllable queueing models, two of three well-known simple N, T and D operating policies are applied alternatingly to the single server controllable queueing models, so called alternating (NT), (ND) and (TD) policies. For example, the alternating (ND) operating policy is defined as the busy period is initiated by the simple N operating policy first, then the next busy period is initiated by the simple D operating policy and repeats the same sequence after that continuously. Because of newly designed operating policies, important system characteristic such as the expected busy and idle periods, the expected busy cycle, the expected number of customers in the system and so on should be redefined. That is, the expected busy and idle periods are redefined as the sum of the corresponding expected busy periods and idle periods initiated by both one of the two simple operating policies and the remaining simple operating policy, respectively. The expected number of customers in the system is represented by the weighted or pooled average of both expected number of customers in the system when the predetermined two simple operating policies are applied in sequence repeatedly. In particular, the expected number of customers in the system could be used to derive the expected waiting time in the queue or system by applying the famous Little's formulas. Most of such system characteristics derived would play important roles to construct the total cost functions per unit time for determination of the optimal operating policies by defining appropriate cost elements to operate the desired queueing systems.

• Journal of the Korean Operations Research and Management Science Society
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• v.17 no.1
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• pp.67-75
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• 1992

In this paper, by using perturbation analysis method, We find the sensitivity of the mean busy cycle with respect to means interrival time in M/M/1 Queue. We show that the Perturbation analysis estimate can be expressed as a sum of the infinitesimal perturbation analysis (IPA) estimate and the effect caused by changes in transition probabilities, thus explaining why IPA estimates are not consistent in general.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.37 no.1
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• pp.96-103
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• 2014

A steady-state controllable M/G/1 queueing model operating under the (TN) policy is considered where the (TN) policy is defined as the next busy period will be initiated either after T time units elapsed from the end of the previous busy period if at least one customer arrives at the system during that time period, or the time instant when Nth customer arrives at the system after T time units elapsed without customers' arrivals during that time period. After deriving the necessary system characteristics such as the expected number of customers in the system, the expected length of busy period and so on, the total expected cost function per unit time in the system operation is constructed to determine the optimal operating policy. To do so, the cost elements associated with such system characteristics including the customers' waiting cost in the system and the server's removal and activating cost are defined. Then, the optimal values of the decision variables included in the operating policies are determined by minimizing the total expected cost function per unit time to operate the system under consideration.