On a Queueing System with Repetition and Pushing Calls Out

2001 ◽  
Vol 33 (3) ◽  
pp. 4
Keyword(s):  
Queueing System

Call Center as Retrial Queueing System

2007 ◽  
Vol 39 (5) ◽  
pp. 37-47 ◽  
Author(s):  
Elena V. Koba ◽  
Svetlana V. Pustovaya
Keyword(s):  
Call Center ◽  
Queueing System ◽  
Retrial Queueing System ◽  
Retrial Queueing

OPTIMIZATION OF MARKOV SYSTEMS OF QUEUEING WITH WAITING TIME IN THE MATLAB SYSTEM

2017 ◽  
pp. 39-47
Author(s):  
Viktor Afonin ◽  
Vladimir Valer'evich Nikulin
Keyword(s):  
Queueing System ◽  
Queueing Systems ◽  
Random Variable ◽  
Model Systems ◽  
Queuing Systems ◽  
Input Flow ◽  
Continuous Random Variable ◽  
Input Stream ◽  
Time Intervals ◽  
Markov Systems

The article focuses on attempt to optimize two well-known Markov systems of queueing: a multichannel queueing system with finite storage, and a multichannel queueing system with limited queue time. In the Markov queuing systems, the intensity of the input stream of requests (requirements, calls, customers, demands) is subject to the Poisson law of the probability distribution of the number of applications in the stream; the intensity of service, as well as the intensity of leaving the application queue is subject to exponential distribution. In a Poisson flow, the time intervals between requirements are subject to the exponential law of a continuous random variable. In the context of Markov queueing systems, there have been obtained significant results, which are expressed in the form of analytical dependencies. These dependencies are used for setting up and numerical solution of the problem stated. The probability of failure in service is taken as a task function; it should be minimized and depends on the intensity of input flow of requests, on the intensity of service, and on the intensity of requests leaving the queue. This, in turn, allows to calculate the maximum relative throughput of a given queuing system. The mentioned algorithm was realized in MATLAB system. The results obtained in the form of descriptive algorithms can be used for testing queueing model systems during peak (unchanged) loads.

Transient Analysis of Finite Capacity N-Policy MX/M/1 Queueing System with Server Start-Up and Time-Out

2018 ◽  
Vol 9 (11) ◽  
pp. 1691-1700
Author(s):  
K. Satish Kumar ◽  
K. Chandan ◽  
V. N. Ramadevi
Keyword(s):  
Transient Analysis ◽  
Queueing System ◽  
Finite Capacity ◽  
Time Out ◽  
Start Up

Research of a Repairable Queueing System with Three Kinds of States

2010 ◽  
Vol 12 (2) ◽  
pp. 149-159
Author(s):  
Xueliang ZHOU ◽  
Gupur Geni
Keyword(s):  
Queueing System ◽  
Repairable Queueing System

On Queue-Length Information in a Tandem Queueing System

2020 ◽  
Author(s):  
Jingwei Ji ◽  
Ricky Roet-Green
Keyword(s):  
Queue Length ◽  
Queueing System

On a Markovian queue with weakly correlated interarrival times

1981 ◽  
Vol 18 (01) ◽  
pp. 190-203 ◽  
Author(s):  
Guy Latouche
Keyword(s):  
Time Distribution ◽  
Queueing System ◽  
Interarrival Time ◽  
Probability Vector ◽  
Common Value ◽  
Markovian Queue ◽  
The Stability ◽  
Number Of Customers ◽  
Stationary Probabilities ◽  
Stationary Probability Vector

A queueing system with exponential service and correlated arrivals is analysed. Each interarrival time is exponentially distributed. The parameter of the interarrival time distribution depends on the parameter for the preceding arrival, according to a Markov chain. The parameters of the interarrival time distributions are chosen to be equal to a common value plus a factor ofε, where ε is a small number. Successive arrivals are then weakly correlated. The stability condition is found and it is shown that the system has a stationary probability vector of matrix-geometric form. Furthermore, it is shown that the stationary probabilities for the number of customers in the system, are analytic functions ofε, for sufficiently smallε, and depend more on the variability in the interarrival time distribution, than on the correlations.

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