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Neuro-fuzzy computing and optimisation results for batch discrete time retrial queue

Author

Listed:
  • Shweta Upadhyaya
  • Geetika Malik
  • Richa Sharma

Abstract

The present investigation involves the application of discrete time bulk entrance recurrent queuing model on asynchronous transfer mode (ATM) technology. This analysis includes the concept of Bernoulli feedback along with priority and impatient customers wherein server may undergo starting failure. Once a service is accomplished, the service provider/server either waits for succeeding customer or leave for a vacation of random time span. The service period, vacation period and retrial period all are presumed to follow general distribution. Firstly, we calculate necessary performance indices using generating function method. Thereafter, we approximate all calculated results with the help of adaptive neuro-fuzzy interface system (ANFIS). Furthermore, we discuss how this model can solve issues related to traffic management and control in ATM networks. Lastly, to make the system more economical, we have computationally analysed the model via particle swarm optimisation (PSO) and genetic algorithm (GA) techniques.

Suggested Citation

  • Shweta Upadhyaya & Geetika Malik & Richa Sharma, 2022. "Neuro-fuzzy computing and optimisation results for batch discrete time retrial queue," International Journal of Mathematics in Operational Research, Inderscience Enterprises Ltd, vol. 23(1), pages 119-146.
    • Handle: RePEc:ids:ijmore:v:23:y:2022:i:1:p:119-146
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