A Simple Learning Scheme for Priority Assignment at a Single-Server Queue

A simple fixed structure learning scheme is proposed for priority assignment at a single-server queue. The server processes two streams of jobs, with random service times (exponentially distributed) in which the parameters are unknown at the start. The optimal priority assignment is asymptotically learned with arbitrary accuracy by properly choosing the algorithm parameters. The scheme has finite memory and is easily implementable. Simulations results are included.