scholarly journals Batch Arrival Queueing Model with Unreliable Server

2018 ◽  
Vol 7 (4.10) ◽  
pp. 269 ◽  
Author(s):  
M. Seenivasan ◽  
K. S.Subasri
Keyword(s):  
Queue Length ◽  
Queuing System ◽  
Subject Classification ◽  
Geometric Method ◽  
Batch Arrival ◽  
Queueing Model ◽  
Unreliable Server ◽  
Matrix Geometric Method

The unreliable server with provision of temporary server in the context of application has been investigated. A temporary server is installed when the primary server is over loaded i.e., a fixed queue length of K-policy customers including the customer with the primary server has been build up. The primary server may breakdown while rendering service to the customers; it is sent for the repair. This type of queuing system has been investigated using Matrix Geometric Method to obtain the probabilities of the system steady state.AMS subject classification number— 60K25, 60K30 and 90B22.  

scholarly journals Queues with two units connected in series with a multiserver bulk service with accessible and non accessible batch in unit II

2014 ◽  
Vol 4 (1) ◽  
Author(s):  
G. Ayyappan ◽  
S. Velmurugan
Keyword(s):  
Service Time ◽  
Subject Classification ◽  
Geometric Method ◽  
Finite Buffer ◽  
Queueing Model ◽  
Service Station ◽  
Matrix Geometric Method ◽  
In Series ◽  
Bulk Service ◽  
Number Of Customers

This paper analyses a queueing model consisting of two units I and II connected in series, separated by a finite buffer of size N. Unit I has only one exponential server capable of serving customers one at a time. Unit II consists of c parallel exponential servers and they serve customers in groups according to the bulk service rule. This rule admits each batch served to have not less than ‘a’ and not more than ‘b’ customers such that the arriving customers can enter service station without affecting the service time if the size of the batch being served is less than ‘d’ ( a ≤ d ≤ b ). The steady stateprobability vector of the number of customers waiting and receiving service in unit I and waiting in the buffer is obtained using the modified matrix-geometric method. Numerical results are also presented. AMS Subject Classification number: 60k25 and 65k30

SIMULATION OF A SEA CARGO PORT AS A QUEUING SYSTEM IN ANYLOGIC

2020 ◽  
Vol 4 (26) ◽  
pp. 59-66
Author(s):  
A. G. Morozkov ◽  
◽  
M. R. Yazvenko ◽  
Keyword(s):  
Simulation Model ◽  
Key Words ◽  
Simulation Modeling ◽  
Queue Length ◽  
System Simulation ◽  
Queuing System ◽  
System Model ◽  
System Capacity ◽  
Mathematical Solution ◽  
Structure Simulation

The article presents simplified queuing system model of freight marine port. The article discusses the basic elements of queuing system, its mathematical solution and structure. Simulation model was created using AnyLogic to analyze an effect of system capacity on queue length. The results were analyzed and the solution for queue optimization was proposed. Key words: queuing system, simulation modeling, AnyLogic, marine port, servers, queue.

scholarly journals Fluid Queue Driven by anM/M/1Queue Subject to Bernoulli-Schedule-Controlled Vacation and Vacation Interruption

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Kolinjivadi Viswanathan Vijayashree ◽  
Atlimuthu Anjuka
Keyword(s):  
Death Process ◽  
Queueing Model ◽  
Fluid Queue ◽  
Stationary Analysis ◽  
Governing System ◽  
Matrix Geometric Method ◽  
Vacation Interruption ◽  
Model Subject ◽  
Steady State Probabilities ◽  
Bernoulli Schedule

This paper deals with the stationary analysis of a fluid queue driven by anM/M/1queueing model subject to Bernoulli-Schedule-Controlled Vacation and Vacation Interruption. The model under consideration can be viewed as a quasi-birth and death process. The governing system of differential difference equations is solved using matrix-geometric method in the Laplacian domain. The resulting solutions are then inverted to obtain an explicit expression for the joint steady state probabilities of the content of the buffer and the state of the background queueing model. Numerical illustrations are added to depict the convergence of the stationary buffer content distribution to one subject to suitable stability conditions.

scholarly journals A M/M/1 Queueing Network with Classical Retrial Policy

2021 ◽  
Vol 12 (1S) ◽  
pp. 329-337
Author(s):  
S. Shanmugasundaram, Et. al.
Keyword(s):  
Steady State ◽  
Queue Length ◽  
Queueing Network ◽  
State Probability ◽  
Queueing Model ◽  
Steady State Probability ◽  
Numerical Examples ◽  
System Length ◽  
Number Of Customers ◽  
Little's Formula

In this paper we study the M/M/1 queueing model with retrial on network. We derive the steady state probability of customers in the network, the average number of customers in the all the three nodes in the system, the queue length, system length using little’s formula. The particular case is derived (no retrial). The numerical examples are given to test the correctness of the model.

scholarly journals Analysis of the State-Dependent Queueing Model and Its Application to Battery Swapping and Charging Stations

2020 ◽  
Vol 12 (6) ◽  
pp. 2343
Author(s):  
Doo Il Choi ◽  
Dae-Eun Lim
Keyword(s):  
Congestion Control ◽  
Queue Length ◽  
Blocking Probability ◽  
Control Policy ◽  
Embedded Markov Chain ◽  
Queueing Model ◽  
Leaky Bucket ◽  
Numerical Examples ◽  
Charging Station ◽  
Mean Waiting Time

This study analyzes the performance of a queue length-dependent overload control policy using a leaky bucket (LB) scheme. This queueing model is applied to the operation of a battery swapping and charging station for electric vehicles (EVs). In addition to the LB scheme, we propose two congestion control policies based on EV queue length thresholds. With these policies, the model determines both EV-arrival and battery-supply intervals, and these depend on the number of EVs waiting in the queue. The queue length distributions, including those at arbitrary epochs, are derived using embedded Markov chain and supplementary variable methods. Performance measures such as blocking probability and mean waiting time are investigated using numerical examples. We study the characteristics of the system using numerical examples and use a cost analysis to investigate situations in which the application of each congestion control policy is advantageous.

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