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Author

V M Chandrasekaran

Bio: V M Chandrasekaran is an academic researcher. The author has contributed to research in topics: Queue. The author has an hindex of 1, co-authored 1 publications receiving 1 citations.
Topics: Queue

Papers
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Journal ArticleDOI
01 Nov 2017
TL;DR: An MX / (a,b) / 1 queueing system with server breakdown without interruption, multiple vacations, setup times and N-policy is considered, and the probability generating function of queue length at arbitrary time epoch is derived.
Abstract: In this paper, we have considered an MX / (a,b) / 1 queueing system with server breakdown without interruption, multiple vacations, setup times and N-policy. After a batch of service, if the size of the queue is ξ (< a), then the server immediately takes a vacation. Upon returns from a vacation, if the queue is less than N, then the server takes another vacation. This process continues until the server finds atleast N customers in the queue. After a vacation, if the server finds at least N customers waiting for service, then the server needs a setup time to start the service. After a batch of service, if the amount of waiting customers in the queue is ξ (≥ a) then the server serves a batch of min(ξ,b) customers, where b ≥ a. We derived the probability generating function of queue length at arbitrary time epoch. Further, we obtained some important performance measures.

2 citations


Cited by
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Journal ArticleDOI
TL;DR: An overview and literature survey on the performance modeling and analysis of single server, general service queueing system with service interruption using supplementary variable technique and factors causing service interruption such as unreliable server and server vacation are presented.
Abstract: In most of the queueing models, service is considered to be complete without any interruption. But in reality, queueing systems are subject to interruptions due to failure of server or any other cause. In the present article, we present an overview and literature survey on the performance modeling and analysis of single server, general service queueing system with service interruption using supplementary variable technique. The factors causing service interruption such as unreliable server and server vacation are elaborated. The brief of supplementary variable technique to establish the queue size distribution is explained for single server non-Markovian queueing models by incorporating the features of service interruption. The basic concepts and review of literature on the queues with server breakdown and/or vacationing server are described. The research works done during last 10 years (2010–2019) on queues with service interruption involving many other key concepts namely Bernoulli vacation, multiple vacation, bulk arrival, discouragement, etc. and queueing scenarios of service interruption are reported. Some specific applications are also highlighted.

16 citations

Journal ArticleDOI
TL;DR: The transient scrutiny of a batch arrival feedback queueing system with balking and two stages of varying service with contrasting levels of service subjected to Bernoulli vacation has been examined in this article.
Abstract: The transient scrutiny of a batch arrival feedback queueing system with balking and two stages of varying service with contrasting levels of service subjected to Bernoulli vacation has been examined in this study. Customers also have the option to decline services and leave the service area if the server is unable to fulfill their request when they arrive. The server may continue to serve the customers, if any, after each service with probability $ \omega $, or it may undergo a vacation with probability $ (1-\omega) $. The service channel may fail temporarily when the server is operating in any phase of service, which is then directed straight to the repair process. The model's steady state results and time-dependent probability generating functions in terms of their Laplace transforms have been derived. The mean queue length and the average time spent in the queue are explicitly determined as performance indicators in the various system states. A few unique cases and specific circumstances have also been presented. Finally, the effect of different parameters on the system's efficiency is then numerically analyzed.