Statistical Analysis of a Telephone Call Center

By L. Brown, Noah Gans, A. Mandelbaum, A. Sakov, Haipeng Shen, Sergey Zeltyn, Linda H. Zhao. 2005

903 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Management Information Systems, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/35083c29c8e7e717cab4207cf81b5f007ce5a6f4

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Citations in the corpus

Swayam: distributed autoscaling to meet SLAs of machine learning inference services with resource efficiency (2017, positive): “The computation times closely follow log-normal distributions, which has been previously observed in similar settings [11, 18].”
Telephone Call Centers: Tutorial, Review, and Research Prospects (2003, methodology): “A comparison of the recent [36, 112, 164] with the older [98, 124, 131] is not straightforward for several reasons.”
Asymptotic approximations for stationary distributions of many-server queues with abandonment (2010, background): “However, statistical analysis of real call centers has shown that both service times and patience times are typically not exponentially distributed [5, 18], thus providing strong motivation for this work.”
Staffing a Call Center with Uncertain Arrival Rate and Absenteeism (2006, background): “Re searchers have been investigating the extent of model parameter uncertainty in contact centers and how to reduce it with better forecasting; e.g., see Avramidis et al. (2004), Brown et al. (2005), Shen and Huang (2005), and references therein.”
Coping with Time‐Varying Demand When Setting Staffing Requirements for a Service System (2007, methodology): “First, with abandonments there are censored data (Brown et al. 2005).”
Diversity in smartphone usage (2010, background): “Willkomm et al. and Brown et al. report that mobile users call duration can be approximately modeled by a lognormal distribution [ 4 , 29].”
Law of large numbers limits for many-server queues (2007, background): “The waiting time functional is of particular interest in the context of call centers, where service targets are often specified in terms of the proportion of calls that experience a wait of less than some given level (see, for example, [2]).”
Fluid limits of many-server queues with reneging (2010, background): “FLUID LIMITS OF MANY-SERVERS QUEUES WITH RENEGING 3 distributed [4, 18], thus providing strong motivation for considering manyserver systems with general service and abandonment distributions.”
Covariance matrix selection and estimation via penalised normal likelihood (2006, methodology): “root transformation is used to make the data distribution close to normal ( Brown et al., 2005 ).”
On Honey Bees and Dynamic Server Allocation in Internet Hosting Centers (2004, methodology): “The inhomogeneous Poisson model was developed by Brown et al. (2002) as a good fit to real data; it is controlled by a variability parameter which is the ratio between the maximum and minimum intensities of the arrival process.”
Forecasting: theory and practice (2020, background): “Another family of models relies on the assumption of a time-inhomogeneous Poisson process adopting fixed (Jongbloed and Koole, 2001; Brown et al., 2005b; Shen and Huang, 2008a; Taylor, 2012) and mixed modelling (Avramidis et al., 2004; Aldor-Noiman et al., 2009; Ibrahim and L’Ecuyer, 2013)…”
SPDE Limits of Many Server Queues (2010, background): “[4] that indicates that the service times are lognormally distributed).”
The Poisson Compound Decision Problem Revisited (2010, background): “Various recommendations for q are given in the literature, the simplest and most common one is q = 0, but the choice q = 0.25 was recommended by Brown et al. (2005, 2010).”
Asymmetric Loss Functions for Forecasting in Criminal Justice Settings (2011, methodology): “For example, one can apply normal regression to forecast the full conditional distribution of the response and then use as the forecast the conditional third quantile of that conditional distribution (Brown et al., 2005).”
Federated Learning With Blockchain for Autonomous Vehicles: Analysis and Design Challenges (2020, methodology): “In fact, λ(t) transforms a Poisson process into a homogeneous Poisson process and consequently, statistical methods for nonhomogeneous Poisson process often transforms the data for their performance analysis [28].”