• Journal of applied mathematics & informatics
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• v.6 no.3
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• pp.943-952
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• 1999

Models of single-server queues with vacation have been widely used to study the performance of many computer communica-tion and production system. In this paper we use the formula for a wide class of vacation policies and multiple types of vacations based on the M/G/1 queue with generalized vacations and exhaustive service. furthermore we derive the waiting times for many complex vacation policies which would otherwise be to analyze. These new results are also applicable to other related queueing models. if they conform with the basic model considered in this paper.

• Journal of the Korean Operations Research and Management Science Society
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• v.27 no.2
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• pp.111-121
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• 2002

In this paper, we analyze an M$^{x}$ /G/1 with three types of vacation periods including setup time. Three types of vacations are : N-policy, single vacation, and multiple vacation. We consider compound poisson arrival process and general service time, where the server starts his service when a setup is completed. We find the PGF of the number of customers in system and LST of waiting time, with welch we obtain their means. A decomposition property for the system sloe and waiting time is described also.

• 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.

• Management Science and Financial Engineering
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• v.12 no.2
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• pp.1-18
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• 2006

We consider an $M^x/G/1$ queueing system with Bernoulli vacation schedule under multiple vacation policy. where after each vacation completion or service completion the server takes sequence of vacations until a batch of new customer arrive. This generalizes both $M^x/G/1$ queueing system with multiple vacation as well as M/G/1 Bernoulli vacation model. We carryout an extensive analysis for the queue size distributions at various epochs. Further attempts have been made to unify the results of related batch arrival vacation models.

• Journal of the Korean Statistical Society
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• v.36 no.2
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• pp.285-297
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• 2007

We consider the $P_{\lambda}^M-service$ policy for an M/G/1 queueing system in which the workload is monitored randomly at discrete points in time. If the level of the workload exceeds a threshold ${\lambda}$ when it is monitored, then the service rate is increased from 1 to M instantaneously and is kept as M until the workload reaches zero. By using level-crossing arguments, we obtain explicit expressions for the stationary distribution of the workload in the system.

• Journal of applied mathematics & informatics
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• v.5 no.1
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• pp.1-12
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• 1998

We consider G/M/1 queues with multiple vacation disci-pline where at the end of every busy period the server stays idle in the system for a period of time called changeover time and then follows a vacation if there is no arrival during the changeover time. The vaca-tion time has a hyperexponential distribution. By using the methods of the shift operator and supplementary variable we explicitly obtain the queue length probabilities at arrival time points and arbitrary time points simultaneously.

• Journal of Korean Institute of Industrial Engineers
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• v.28 no.3
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• pp.319-330
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• 2002

We consider BMAP/G/1 queueing system with N-policy and multiple vacations. We derive the vector generating functions of the queue length in a factorized form and interpret the factorization. We also derive the mean queue length.

• 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.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2007.11a
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• pp.195-203
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• 2007

In this paper, an analytic model of sleep model operation in the IEEE 802.16e is investigated. A mobile subscribe. station (MSS) goes to sleep mode after negotiations with the base station (BS) and wakes up periodically for a short interval to check whether there is downlink traffic to it. If the arrival of traffic is notified, an MSS returns to wake mode. Otherwise, it again enters increased sleep interval which is double as the previous one. In order to consider the situation more practically, we propose the sleep mode starting threshold, during which MSS should await packets before it enters the sleep mode. By modifying the M/G/1 with multiple vacations model, energy consumption ratio(ECR) and average packet response time are calculated. Our analytic model provides potential guidance in determining the optimal parameters values such as sleep mode starting threshold, minimal sleep and maximal sleep window.

• Korean Management Science Review
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• v.21 no.3
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• pp.39-54
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• 2004

This paper presents a multi-agent system for intelligent recommendation of travel plans to users. The goal of the system is to provide alternative and preferable travel plans to users when the availability of tickets is low such as in vacations, holidays, weekends, or peak seasons. The multiple agents in the system search for available alternatives for a target travel in collaboration with other agents and recommend best alternatives by analyzing them using a multi-criteria decision-making model. A prototype online travel support system was constructed and a simulation experiment was performed for evaluation and comparison with different travel planning strategies.