Specialized and flexible servers subject to the effects of learning and forgetting

We consider learning and forgetting in the context of two-server queueing systems and evaluate the tradeoff between utilizing specialized and flexible servers. Specifically, we investigate the performance of two queueing systems. The first system utilizes a specialized workforce where every server handles one job type. The specialized workforce splits the system into two queues where the dedicated servers capitalize on the learning process and consequently reduce the service time. The second system utilizes a flexible workforce where a server can handle any job type. The flexible workforce system allows for the servers to be arranged in parallel where alternating job types results in forgetting and accordingly an increase in service time. A numerical study investigates the impact of the workforce policies on the system performance measures.