Optimal service policies under learning effects

For high‐value workforces in service organisations 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; cross‐training boosts system capacity the most; and no simple policy matches a dynamic optimal cost policy under all conditions.