Publication Type

Journal Article

Version

Postprint

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

Additional URL

http://doi.org/10.1016/j.cor.2016.06.005