Communications of the ACM - Services science
A research manifesto for services science
Communications of the ACM - Services science
Understanding service sector innovation
Communications of the ACM - Services science
What academic research tells us about service
Communications of the ACM - Services science
Semantics to energize the full services spectrum
Communications of the ACM - Services science
Resource planning for business services
Communications of the ACM - Services science
Communications of the ACM - Services science
The evolution and discovery of services science in business schools
Communications of the ACM - Services science
Service systems, service scientists, SSME, and innovation
Communications of the ACM - Services science
The Clarion Call for modern services: China, Japan, Europe, and the U.S.
Communications of the ACM - Services science
Modeling and simulation of call centers
WSC '05 Proceedings of the 37th conference on Winter simulation
The Impact of Increased Employee Retention on Performance in a Customer Contact Center
Manufacturing & Service Operations Management
Managing Response Time in a Call-Routing Problem with Service Failure
Operations Research
Firefighter Staffing Including Temporary Absences and Wastage
Operations Research
SimMan-A simulation model for workforce capacity planning
Computers and Operations Research
An indirect workforce (re)allocation model for semiconductor manufacturing
Proceedings of the 40th Conference on Winter Simulation
Selecting the appropriate input data set when configuring a permanent workforce
Computers and Industrial Engineering
Journal of Intelligent Manufacturing
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
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We study the employee staffing problem in a service organization that uses employee service capacity to meet random, nonstationary service requirements. The employees experience learning and turnover on the job, and we develop a Markov Decision Process (MDP) model which explicitly represents the stochastic nature of these effects. Theoretical results show that the optimal hiring policy is of a state-dependent "hire-up-to" type, similar to an inventory "order-up-to" policy. For two important special cases, a myopic policy is optimal. We also test a linear programming (LP) based heuristic, which uses average learning and turnover behavior, in stationary environments. In most cases, the LP-based policy performs quite well, within 1% of optimality. When flexible capacity--in the form of overtime or outsourcing--is expensive or not available, however, explicit modeling of stochastic learning and turnover effects may improve performance significantly.