Publication Type
Journal Article
Version
acceptedVersion
Publication Date
12-2016
Abstract
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.
Keywords
Data Analytics for Queues, Simulation, Nonlinear regression, Alternating Conditional Expectation
Discipline
Operations and Supply Chain Management | Operations Research, Systems Engineering and Industrial Engineering
Research Areas
Operations Management
Publication
Computers and Operations Research
Volume
76
First Page
33
Last Page
42
ISSN
0305-0548
Identifier
10.1016/j.cor.2016.06.005
Publisher
Elsevier
Citation
YANG, Kum Khiong; TUGBA, Cayirli; and LOW, Mei Wan.
Predicting the Performance of Queues: A Data Analytic Approach. (2016). Computers and Operations Research. 76, 33-42.
Available at: https://ink.library.smu.edu.sg/lkcsb_research/4944
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1016/j.cor.2016.06.005
Included in
Operations and Supply Chain Management Commons, Operations Research, Systems Engineering and Industrial Engineering Commons