Author
Yang, K. K., Çayırlı, Tuğba, Low, J. M.W.
Publication Date
2016
Publication Place
-
Elsevier
Subject
Data analytics for queues, Simulation, Nonlinear regression, Alternating conditional expectation
Type
Periodical
Language
English
Digital
Yes
Manuscript
No
Library
Özyeğin University
Library Asset ID
2-s2.0-84976591686
Record ID
19de4e03-1e11-49ba-9104-b269edbce879
Library Location
Business Administration
Date
2016
Notes
Due to copyright restrictions, the access to the full text of this article is only available via subscription.
Sample Text
Existing models of multi-server queues with system transience and non-standard assumptions are either too complex or restricted in their assumptions to be used broadly in practice. This paper proposes using data analytics, combining computer simulation to generate the data and an advanced non-linear regression technique called the Alternating Conditional Expectation (ACE) to construct a set of easy-to-use equations to predict the performance of queues with a scheduled start and end time. Our results show that the equations can accurately predict the queue performance as a function of the number of servers, mean arrival load, session length and service time variability. To further facilitate its use in practice, the equations are developed into an open-source online tool accessible at http://singlequeuesystemstool.com/. The proposed procedure of data analytics can be used to model other more complex systems.
DOI
10.1016/j.cor.2016.06.005
Cilt
76
