Performance prediction and ANFIS computing for unreliable retrial queue with delayed repair under modified vacation policy

Shweta Upadhyaya and Chetna Kushwaha

International Journal of Mathematics in Operational Research, 2020, vol. 17, issue 4, 437-466

Abstract: This paper deals with the analysis of M^X/G/1 retrial queue with impatient customers, modified vacation policy and Bernoulli feedback. When the incoming customer finds the server busy, on vacation or in the state of breakdown, he joins the virtual queue called retrial orbit, otherwise the service is provided to the customer who is at the head of the queue. The service is provided in l(0 ≤ i ≤ l) phases where first is compulsory service and remaining services are optional. When the system becomes empty, server leaves for the vacation of arbitrary length and can take at most J number of vacations. When server comes back from the vacation and finds at least one customer in the queue, he starts providing service to the customer. Supplementary variable technique (SVT) and probability generating function (PGF) method is used to derive the system size distribution and other performance indices. We have also approximated the analytical results using adaptive neuro-fuzzy interface system (ANFIS) soft computing technique, which can identify parameters using supervised learning methods.

Keywords: M X /G/1 retrial queue; Bernoulli feedback; multi-optional services; server breakdown; delayed repair; modified vacation policy; system size; ANFIS. (search for similar items in EconPapers)
Date: 2020
References: Add references at CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://www.inderscience.com/link.php?id=110843 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijmore:v:17:y:2020:i:4:p:437-466

Access Statistics for this article

More articles in International Journal of Mathematics in Operational Research from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().