Dynamic routing policies for multiskill call centers
Probability in the Engineering and Informational Sciences
Cross-Selling in a Call Center with a Heterogeneous Customer Population
Operations Research
Scheduling Flexible Servers with Convex Delay Costs in Many-Server Service Systems
Manufacturing & Service Operations Management
Queue-and-Idleness-Ratio Controls in Many-Server Service Systems
Mathematics of Operations Research
On a Data-Driven Method for Staffing Large Call Centers
Operations Research
Priority Assignment Under Imperfect Information on Customer Type Identities
Manufacturing & Service Operations Management
Queueing game models for differentiated services
GameNets'09 Proceedings of the First ICST international conference on Game Theory for Networks
Fair Dynamic Routing in Large-Scale Heterogeneous-Server Systems
Operations Research
When Promotions Meet Operations: Cross-Selling and Its Effect on Call Center Performance
Manufacturing & Service Operations Management
Control of systems with flexible multi-server pools: a shadow routing approach
Queueing Systems: Theory and Applications
Queues with waiting time dependent service
Queueing Systems: Theory and Applications
State Space Collapse in Many-Server Diffusion Limits of Parallel Server Systems
Mathematics of Operations Research
Routing to Manage Resolution and Waiting Time in Call Centers with Heterogeneous Servers
Manufacturing & Service Operations Management
The Learning Curve of IT Knowledge Workers in a Computing Call Center
Information Systems Research
Proceedings of the Winter Simulation Conference
Computers and Industrial Engineering
On the numerical solution of Kronecker-based infinite level-dependent QBD processes
Performance Evaluation
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We study large-scale service systems with multiple customer classes and many statistically identical servers. The following question is addressed: How many servers are required (staffing) and how does one match them with customers (control) to minimize staffing cost, subject to class-level quality-of-service constraints? We tackle this question by characterizing scheduling and staffing schemes that are asymptotically optimal in the limit, as system load grows to infinity. The asymptotic regimes considered are consistent with the efficiency-driven (ED), quality-driven (QD), and quality-and-efficiency-driven (QED) regimes, first introduced in the context of a single-class service system. Our main findings are as follows: (a) Decoupling of staffing and control, namely, (i) staffing disregards the multiclass nature of the system and is analogous to the staffing of a single-class system with the same aggregate demand and a single global quality-of-service constraint, and (ii) class-level service differentiation is obtained by using a simple idle-server-based threshold-priority (ITP) control (with state-independent thresholds); and (b) robustness of the staffing and control rules: our proposed single-class staffing (SCS) rule and ITP control are approximately optimal under various problem formulations and model assumptions. Particularly, although our solution is shown to be asymptotically optimal for large systems, we numerically demonstrate that it performs well also for relatively small systems.