Antons, OliverOliverAntonsArlinghaus, JuliaJuliaArlinghaus2022-07-062022-07-062021https://publica.fraunhofer.de/handle/publica/41865210.1007/978-3-030-69373-2_13This paper studies the potentials of learning and benefits of local data processing in a distributed control setting. We deploy a multi-agent system in the context of a discrete-event simulation to model distributed control for a job shop manufacturing system with variable processing times and multi-stage production processes. Within this simulation, we compare queue length estimation as dispatching rule against a variation with learning capability, which processes additional historic data on a machine agent level, showing the potentials of learning and coordination for distributed control in PPC.enconference paper