THESIS
2015
xii, 90 pages : illustrations ; 30 cm
Abstract
Call centers have become an integral part of modern business to facilitate interactions between
customers and companies. To determine a proper staffing level is crucial for successful
operations of a call center. This workforce management process aims at enhancing the customer
service quality and reducing operational cost. In this thesis, two related research questions are
studied from different aspects.
The first question concentrates on forecasting call center workload. Our research proposes
a new doubly stochastic Poisson process model for call center arrivals to capture both the fixed
arrival pattern and the over-dispersed randomness of call arrival process. This model treats
the uncertainty of the arrival rate as dynamic while many existing models consider it static. A
Bay...[
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Call centers have become an integral part of modern business to facilitate interactions between
customers and companies. To determine a proper staffing level is crucial for successful
operations of a call center. This workforce management process aims at enhancing the customer
service quality and reducing operational cost. In this thesis, two related research questions are
studied from different aspects.
The first question concentrates on forecasting call center workload. Our research proposes
a new doubly stochastic Poisson process model for call center arrivals to capture both the fixed
arrival pattern and the over-dispersed randomness of call arrival process. This model treats
the uncertainty of the arrival rate as dynamic while many existing models consider it static. A
Bayesian approach via the Markov chain Monte Carlo method is developed for the parameter
estimation as well as forecasting. In addition, we consider intra-day updating of the arrival
forecasts, a challenge faced by call center managers in real time. A particle filtering method is
proposed to update the posterior probability distribution of the arrival rate in a sequential manner
given the new observed arrival counts. Numerical results demonstrate that our forecasting
approach is accurate and outperforms existing models.
The second part investigates the calibration problem of call center performance measures.
In our work, a semi-parametric framework with shape constraints is proposed to calibrate a theoretical
queueing model to match empirical performance measures. This calibration framework
integrates the implicit domain knowledge of call center operations in the empirical dataset and
the theoretical queueing system models. With the additive form of our framework, the calibration
functions reveal the difference between the strict queueing theory assumptions and the
empirical operations. The calibrated service performance fit both the simulated and empirical data well and is easy to interpret. In addition, a staffing level can be uniquely determined after
the calibration to meet the target service quality. The service quality of a simulated call center
operations with calibrated staffing level is close to the pre-set target. Finally, we simulate call
center operations with real arrival data and find that our staffing policy delivers a system performance
that is much closer to the target service quality than many commonly used staffing
policies.
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