The Autonomous Robot Orchestration Solution (AROS) is transforming the management of robot fleets by identifying the state and environment of each robot and enabling them to collaborate toward common goals. This paper introduces AROS and its application in controlling massive fleets of Overhead Hoist Transport (OHT) vehicles in semiconductor fabrication facilities. AROS leverages key technologies, including reinforcement learning algorithms, discrete event simulation, and real-time data collection through Digital Twin (DT). The DT replicates the real system in a virtual environment with realtime communication to optimize decision-making for OHTs. A key innovation of AROS is the introduction of active Q routing, a dynamic routing method that adapts to changing traffic conditions by predicting and adjusting travel times through discrete event simulation. Active Q routing enhances operational efficiency by mitigating congestion and reducing delays, even in highly dynamic environments. We demonstrate the effectiveness of AROS and active Q routing on OHT system performance, showcasing reductions in average delivery times and increases in delivery capacity. These findings are validated through real-world use cases in a large-scale semiconductor fab.