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In most service systems, the servers are humans who desire to experience acertain level of idleness. In call centers, this manifests itself as the callavoidance behavior, where servers strategically adjust their service rate tostrike a balance between the idleness they receive and effort to work harder.Moreover, being humans, each server values this trade-off differently and hasdifferent capabilities. Drawing ideas on mean-field games we develop a novelframework relying on measure-valued processes to simultaneously addressstrategic server behavior and inherent server heterogeneity in service systems.This framework enables us to extend the recent literature on strategic serversin four new directions by: (i) incorporating individual choices of servers,(ii) incorporating individual abilities of servers, (iii) modeling thediscomfort experienced by servers due to low levels of idleness, and (iv)considering more general routing policies. Using our framework, we are able toasymptotically characterize asymmetric Nash equilibria for many-server systemswith strategic servers.  In simpler cases, it has been shown that the purely quality-driven regime isasymptotically optimal. However, we show that if the discomfort increases fastenough as the idleness approaches zero, the quality-and-efficiency-drivenregime and other quality driven regimes can be optimal. This is the first timethis conclusion appears in the literature.

Many-Server Queueing Systems with Heterogeneous Strategic Servers in Heavy Traffic