Optimal service policies under learning effects

For high-value workforces in service organizations such as call centres, scheduling rules rely increasingly on queueing system models to achieve optimal performance. Most of these models assume a homogeneous population of servers, or at least a static service capacity per service agent. In this work we examine the challenge posed by dynamically fluctuating service capacity, where servers may increase their own service efficiency through experience; they may also decrease it through absence. We analyse the special case of a single agent selecting between two different job classes, and examine which of five service allocation policies performs best in the presence of learning and forgetting effects. We find that a type of specialisation minimises the steady state queue size; crosstraining boosts system capacity the most; and no simple policy matches a dynamic optimal cost policy under all conditions.