Finite Queueing Model under Uncertainty: Application on the Corona-Virus (COVID-19) Patients | Asian

This work presents a method for adding epistemic uncertainty in computing retrial queueing model performance metrics that is based on Chao Polynomials Expansion, as opposed to Taylor series Expansion. The method entails decomposing the model's output (stationary distribution of the model) into polynomial chaos. The suggested method is used to corona-virus illness patients to examine the M/M/1 Retrial Queue with Working Vacations and Vacation Interruption. Finally, there are some numerical examples and a cost optimization study.

Please see the link :- https://globalpresshub.com/index.php/AJPAM/article/view/1369