Difference Equations Approach for Multi-Server Queueing Models with Removable Servers

James J. Kim (), Douglas G. Down (), Mohan Chaudhry () and Abhijit Datta Banik ()
Additional contact information
James J. Kim: Royal Canadian Air Force (RCAF)
Douglas G. Down: McMaster University
Mohan Chaudhry: Royal Military College of Canada
Abhijit Datta Banik: Indian Institute of Technology

Methodology and Computing in Applied Probability, 2022, vol. 24, issue 3, 1297-1321

Abstract: Abstract We consider an extended form of the MX/M/c queue with two types of server groups: Static as well as dynamic (which turn on/off in a state-dependent manner) servers. The two server groups may have homogenous or non-homogenous service rates. The model is further extended to feature setup and delayed-off times, finite capacity, and k staffing levels. This class of queues is solved via the difference equations approach, which addresses narratives in the literature and achieves higher numerical efficiency than the direct method. While the model of this queueing system is not new, the methodology for solving it is. Comparisons between our model and classic queues are provided followed by concluding remarks, including a summary of key observations.

Keywords: Multi-server; Dynamic servers; Setup; Delayed-off; k staffing; Difference equations; System performance; Resource consumption (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s11009-021-09848-8 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

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:spr:metcap:v:24:y:2022:i:3:d:10.1007_s11009-021-09848-8

Ordering information: This journal article can be ordered from
https://www.springer.com/journal/11009

DOI: 10.1007/s11009-021-09848-8

Access Statistics for this article

Methodology and Computing in Applied Probability is currently edited by Joseph Glaz

More articles in Methodology and Computing in Applied Probability from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().