Predicting the performance of queues–A data analytic approach

Name: Predicting the performance of queues–A data analytic approach
Author: Yang, K. K., Çayırlı, Tuğba, Low, J. M.W.

İsim	Predicting the performance of queues–A data analytic approach
Yazar	Yang, K. K., Çayırlı, Tuğba, Low, J. M.W.
Basım Tarihi:	2016
Basım Yeri	- Elsevier
Konu	Data analytics for queues, Simulation, Nonlinear regression, Alternating conditional expectation
Tür	Süreli Yayın
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	2-s2.0-84976591686
Kayıt Numarası	19de4e03-1e11-49ba-9104-b269edbce879
Lokasyon	Business Administration
Tarih	2016
Notlar	Due to copyright restrictions, the access to the full text of this article is only available via subscription.
Örnek Metin	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

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Predicting the performance of queues–A data analytic approach

Yazar Yang, K. K., Çayırlı, Tuğba, Low, J. M.W.

Basım Tarihi 2016

Basım Yeri - Elsevier

Konu Data analytics for queues, Simulation, Nonlinear regression, Alternating conditional expectation

Tür Süreli Yayın

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 2-s2.0-84976591686

Kayıt Numarası 19de4e03-1e11-49ba-9104-b269edbce879

Lokasyon Business Administration

Tarih 2016

Notlar Due to copyright restrictions, the access to the full text of this article is only available via subscription.

Örnek Metin 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