Call center simulation in Bell Canada
Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 2
Commissioned Paper: Telephone Call Centers: Tutorial, Review, and Research Prospects
Manufacturing & Service Operations Management
Modeling Daily Arrivals to a Telephone Call Center
Management Science
Analysis of call centre arrival data using singular value decomposition: Research Articles
Applied Stochastic Models in Business and Industry
Modeling and simulation of call centers
WSC '05 Proceedings of the 37th conference on Winter simulation
A java library for simulating contact centers
WSC '05 Proceedings of the 37th conference on Winter simulation
Performance measures for service systems with a random arrival rate
WSC '05 Proceedings of the 37th conference on Winter simulation
Modeling and Optimization Problems in Contact Centers
QEST '06 Proceedings of the 3rd international conference on the Quantitative Evaluation of Systems
Forecast errors in service systems
Probability in the Engineering and Informational Sciences
Modeling and Analysis of Call Center Arrival Data: A Bayesian Approach
Management Science
Interday Forecasting and Intraday Updating of Call Center Arrivals
Manufacturing & Service Operations Management
A Simple Staffing Method for Multiskill Call Centers
Manufacturing & Service Operations Management
Dynamic staffing in a telephone call center aiming to immediately answer all calls
Operations Research Letters
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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.