scholarly journals Optimal batch process admission control in tandem queueing systems with queue time constraint considerations

2012 ◽  
Author(s):  
Cheng-Hung Wu ◽  
Yu-Ching Cheng ◽  
Ping-Ju Tang ◽  
Jiun-Yu Yu
Keyword(s):  
Admission Control ◽  
Queueing Systems ◽  
Batch Process ◽  
Time Constraint ◽  
Optimal Batch

Optimal batch process development

1996 ◽  
Vol 20 (6-7) ◽  
pp. 885-896 ◽  
Author(s):  
R.J. Allgor ◽  
M.D. Barrera ◽  
P.I. Barton ◽  
L.B. Evans
Keyword(s):  
Process Development ◽  
Batch Process ◽  
Optimal Batch

Optimal admission control for M/D/1/K queueing systems

1999 ◽  
Vol 50 (1) ◽  
pp. 91-100 ◽  
Author(s):  
Hans Daduna ◽  
Pavel S. Knopov
Keyword(s):  
Admission Control ◽  
Queueing Systems

A Novel Resource Allocation and Admission Control in LTE Systems

2010 ◽  
Vol E93-B (3) ◽  
pp. 721-724
Author(s):  
Abhishek ROY ◽  
Navrati SAXENA ◽  
Jitae SHIN
Keyword(s):  
Resource Allocation ◽  
Admission Control

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.

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