The physics of the Mt/G/ ∞ symbol Queue
Operations Research
Resource sharing for book-ahead and instantaneous-request calls
IEEE/ACM Transactions on Networking (TON)
Improving Service by Informing Customers About Anticipated Delays
Management Science
Control and recovery from rare congestion events in a large multi-server system
Queueing Systems: Theory and Applications
A Markov Renewal Based Model for Wireless Networks
Queueing Systems: Theory and Applications
Should we model dependence and nonstationarity, and if so how?
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
Variance reduction in the simulation of call centers
Proceedings of the 38th conference on Winter simulation
Forecast errors in service systems
Probability in the Engineering and Informational Sciences
Workload Management in Dynamic IT Service Delivery Organizations
DSOM '09 Proceedings of the 20th IFIP/IEEE International Workshop on Distributed Systems: Operations and Management: Integrated Management of Systems, Services, Processes and People in IT
Proactive resource provisioning
Computer Communications
On the modeling and forecasting of call center arrivals
Proceedings of the Winter Simulation Conference
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This paper proposes practical modeling and analysis methods to facilitate dynamic staffing in a telephone call center with the objective of immediately answering all calls. Because of this goal, it is natural to use infinite-server queueing models. These models are very useful because they are so tractable. A key to the dynamic staffing is exploiting detailed knowledge of system state in order to obtain good estimates of the mean and variance of the demand in the near future. The near-term staffing needs, e.g., for the next minute or the next 20 min., can often be predicted by exploiting information about recent demand and current calls in progress, as well as historical data. The remaining holding times of calls in progress can be predicted by classifying and keeping track of call types, by measuring holding-time distributions and by taking account of the elapsed holding times of calls in progress. The number of new calls in service can be predicted by exploiting information about both historical and recent demand.