REal-time delay estimation in call centers

Ibrahim, Ibrahim; Whitt,

doi:10.1109/wsc.2008.4736409

Cited by 9 publications

(7 citation statements)

References 7 publications

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“…A second branch of research focuses exclusively on delay predictions, without considering the impact on the behavior of customers. The work of Whitt (1999b), Ibrahim and Whitt (2008, 2009a, 2009b, 2010, 2011a, 2011b, and Ibrahim, Armony, and Bassamboo (2015), as well as the present paper, fall into this category.…”

Section: Literature Reviewmentioning

confidence: 81%

Waiting time predictors for multi-skill call centers

Thiongane¹,

Chan²,

L’Ecuyer³

2015

2015 Winter Simulation Conference (WSC)

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We develop customer delay predictors for multi-skill call centers that take into inputs the queueing state upon arrival and the waiting time of the last customer served. Many predictors have been proposed and studied for the single queue system, but barely any predictor currently exists for the multi-skill case. We introduce two new predictors that use cubic regression splines and artificial neural networks, respectively, and whose parameters are optimized (or learned) from observation data obtained by simulation. In numerical experiments, our proposed predictors are much more accurate than a popular heuristic that uses as a predictor the delay of the last customer of the same type that started service.

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Section: Literature Reviewmentioning

confidence: 81%

Waiting time predictors for multi-skill call centers

Thiongane¹,

Chan²,

L’Ecuyer³

2015

2015 Winter Simulation Conference (WSC)

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“…The graphs also show another peak at 60 seconds after an announcement indicating a customer's position in the queue. The issue of delay estimating and announcing delay times, and the effect it can have on queues is evaluated in Ibrahim & Whitt (2008) and (Huang, Mandelbaum, et al (2017). The hazard function shows a general decline over the range of values plotted (0 to 400 secs.)…”

Section: The Erlang a Modelmentioning

confidence: 99%

Evaluating the Performance of the Erlang Models for Call Centers

Robbins¹

2019

IJAST

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In this paper we evaluate two queuing models used to analyze call centers; the Erlang C and Erlang A models. The Erlang C is a simple model that ignores caller abandonment and is the most commonly used model. The Erlang A model allows for abandonment, but performance measures are more difficult to calculate. We compare the theoretical performance predictions of these models to a steady state simulation model where many of the simplifying assumptions used in standard analytical models are relaxed. Our findings support the assertion that the Erlang A model is more accurate, but we find that in contrast to the Erlang C model, Erlang A tends to be optimistically biased. Our findings indicate that neither model clearly dominates the other in all situations and that care must be taken to select the correct model based on call center conditions and the intended purpose of the model.

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“…There is a vast literature on this subject, but our focus is quite different. Work by Ibrahim and Whitt [14,15,16,17] develops new real-time estimators for estimating delays in various queueing systems. The work of Armony and Maglaras [3], Guo and Zipkin [9], Hassin [11], Armony et al [4], Guo and Zipkin [10], Jouini et al [20,21], Allon and Bassamboo [1], Allon et al [2], Ibrahim et al [18], Whitt [31] and references therein analyzes the impact of delay announcements on the queueing process and the abandonment process of the system.…”

Section: Introductionmentioning

confidence: 99%

An analysis of queues with delayed information and time-varying arrival rates

2018

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Understanding how delayed information impacts queueing systems is an important area of research. However, much of the current literature neglects one important feature of many queueing systems, namely non-stationary arrivals. Non-stationary arrivals model the fact that customers tend to access services during certain times of the day and not at a constant rate. In this paper, we analyze two two-dimensional deterministic fluid models that incorporate customer choice behavior based on delayed queue length information with time varying arrivals. In the first model, customers receive queue length information that is delayed by a constant ∆. In the second model, customers receive information about the queue length through a moving average of the queue length where the moving average window is ∆. We analyze the impact of the time varying arrival rate and show using asymptotic analysis that the time varying arrival rate does not impact the critical delay unless the frequency of the time varying arrival 1 arXiv:1701.05443v1 [math.DS]

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REal-time delay estimation in call centers

Cited by 9 publications

References 7 publications

Waiting time predictors for multi-skill call centers

Waiting time predictors for multi-skill call centers

Evaluating the Performance of the Erlang Models for Call Centers

An analysis of queues with delayed information and time-varying arrival rates

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