Call center simulation in Bell Canada

Tanir, Oryal; Booth, Richard J.

doi:10.1145/324898.325351

Cited by 18 publications

(10 citation statements)

References 2 publications

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“…Our development is influenced by two properties of call center arrivals observed in recent empirical studies, namely, a variance that is considerably higher than implied by Poisson arrivals (Jongbloed and Koole 2001) and strong positive association between the arrivals in different periods within the same day (Tanir and Booth 1999). The models extend those proposed recently by Jongbloed and Koole (2001) and Whitt (1999).…”

Section: Introductionsupporting

confidence: 57%

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Modeling Daily Arrivals to a Telephone Call Center

2004

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We develop stochastic models of time-dependent arrivals, with focus on the application to call centers. Our models reproduce three essential features of call center arrivals observed in recent empirical studies: a variance larger than the mean for the number of arrivals in any given time interval, a time-varying arrival intensity over the course of a day, and nonzero correlation between the arrival counts in different periods within the same day. For each of the new models, we characterize the joint distribution of the vector of arrival counts, with particular focus on characterizing how the new models are more flexible than standard or previously proposed models. We report empirical results from a study on arrival data from a real-life call center, including the essential features of the arrival process, the goodness of fit of the estimated models, and the sensitivity of various simulated performance measures of the call center to the choice of arrival process model.

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Section: Introductionsupporting

confidence: 57%

“…The arrival rate varies considerably with the time of day (see Tanir andBooth 1999, p. 1643;Deslauriers 2003). …”

Section: Introductionmentioning

confidence: 99%

Modeling Daily Arrivals to a Telephone Call Center

2004

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“…There is significant (strong) positive dependence between arrivals rates (or counts) in different time periods of the same day, and this positive dependence usually decreases when the considered time Ibrahim, L'Ecuyer, Regnard, and Shen periods are taken farther apart (Tanir and Booth 1999, Whitt 1999, Avramidis, Deslauriers, and L'Ecuyer 2004, Shen and Huang 2008b, Channouf and L'Ecuyer 2012 In call centers with multiple call types, there is sometimes strong dependence between arrival rates (and counts) of certain call types during the same time period.…”

Section: Key Features Of Arrival Processesmentioning

confidence: 99%

On the modeling and forecasting of call center arrivals

Ibrahim

Han

L’Ecuyer

et al. 2012

Proceedings Title: Proceedings of the 2012 Winter Simulation Conference (WSC)

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We review and discuss the key issues in building statistical models for the call arrival process in telephone call centers, and then we survey and compare various types of models proposed so far. These models are used both for simulation and to forecast incoming call volumes to make staffing decisions and build (or update) work schedules for agents who answer those calls. Commercial software and call center managers usually base their decisions solely on point forecasts, given in the form of mathematical expectations (conditional on current information), but distributional forecasts, which come in the form of (conditional) probability distributions, are generally more useful, in particular in the context of simulation. Building realistic models is not simple, because arrival rates are themselves stochastic, time-dependent, dependent across time periods and across call types, and are often affected by external events. As an illustration, we evaluate the forecasting accuracy of selected models in an empirical study with real-life call center data.

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“…From empirical study, call center arrivals are known to have a variance that is considerably higher than implied by Poisson arrival (Jongbloed andKoole 2001 andDeslauriers 2003) and strong positive association between the arrivals in different time periods (Tanir andBooth 1999 andBrown et al 2002).…”

Section: Difficulties Encounteredmentioning

confidence: 99%

“…This assumption rarely holds in practice (Tanir and Booth 1999). Avramidis, Deslauriers, and L'Ecuyer (2003) model the i in (1) as dependent random variables where The idea is to let the random factor W account for the day-today traffic variation.…”

Section: Arrival Processesmentioning

confidence: 99%

Modelling and simulation of a telephone call center

Pichitlamken¹,

Deslauriers²,

L’Ecuyer³

et al.

Proceedings of the 2003 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.03EX693)

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We consider a system with two types of traffic and two types of agents. Outbound calls are served only by blend agents, whereas inbound calls can be served by either inboundonly or blend agents. Our objective is to allocate a number of agents such that some service requirement is satisfied. We have taken two approaches in analyzing this staffing problem: We developed a simulation model of the call center, which allows us to do a what-if analysis, as well as continuous-time Markov chain (CTMC) queueing models, which provide approximations of system performance measures. We describe the simulation model in this paper.

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Call center simulation in Bell Canada

Cited by 18 publications

References 2 publications

Modeling Daily Arrivals to a Telephone Call Center

Modeling Daily Arrivals to a Telephone Call Center

On the modeling and forecasting of call center arrivals

Modelling and simulation of a telephone call center

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