In this paper, the effect that routing rules have on agent learning is researched.
A nonlinear optimization framework for two kinds of expertise objectives are developed: one that seeks equal distribution of experience across the workforce (effectively cross-training) and one that aims to develop specialized expertise by prioritizing the routing of specific customer inquiries to specific agents. Analytical models of call center operations are inadequate to handle this task, so instead we turn to discrete-event simulation, and evaluate the effect of routing policies on agent expertise with a custom simulator developed in the ExtendSim modeling environment. Simulation results describe an efficient frontier in routing policies that depends on the underlying expertise objective function.