Designing a Call Center with Impatient Customers
Manufacturing & Service Operations Management
Commissioned Paper: Telephone Call Centers: Tutorial, Review, and Research Prospects
Manufacturing & Service Operations Management
Dimensioning Large Call Centers
Operations Research
Modeling Daily Arrivals to a Telephone Call Center
Management Science
A Method for Staffing Large Call Centers Based on Stochastic Fluid Models
Manufacturing & Service Operations Management
Queueing Systems: Theory and Applications
A Staffing Algorithm for Call Centers with Skill-Based Routing
Manufacturing & Service Operations Management
Service-Level Differentiation in Call Centers with Fully Flexible Servers
Management Science
Staffing Multiskill Call Centers via Linear Programming and Simulation
Management Science
Staffing of Time-Varying Queues to Achieve Time-Stable Performance
Management Science
Pointwise Stationary Fluid Models for Stochastic Processing Networks
Manufacturing & Service Operations Management
Responding to Unexpected Overloads in Large-Scale Service Systems
Management Science
Control of systems with flexible multi-server pools: a shadow routing approach
Queueing Systems: Theory and Applications
Shadow-Routing Based Control of Flexible Multiserver Pools in Overload
Operations Research
The Learning Curve of IT Knowledge Workers in a Computing Call Center
Information Systems Research
Analysis of operational data to improve performance in service delivery systems
Proceedings of the 8th International Conference on Network and Service Management
Data-stories about (im)patient customers in tele-queues
Queueing Systems: Theory and Applications
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We consider a call center model with multiple customer classes and multiple server pools. Calls arrive randomly over time, and the instantaneous arrival rates are allowed to vary both temporally and stochastically in an arbitrary manner. The objective is to minimize the sum of personnel costs and expected abandonment penalties by selecting an appropriate staffing level for each server pool. We propose a simple and computationally tractable method for solving this problem that requires as input only a few system parameters and historical call arrival data for each customer class; in this sense the method is said to be data-driven. The efficacy of the proposed method is illustrated via numerical examples. An asymptotic analysis establishes that the prescribed staffing levels achieve near-optimal performance and characterizes the magnitude of the optimality gap.