The flexible flow shop refers to such a manufacturing environment in which jobs are to be processed through serial stages, with one or multiple machines available at each stage. It is usually a complex task when specific objective is demanded such as minimum cost, minimum time, etc. Static scheduling of such problems has been much researched, however, little efforts have been made on realtime scheduling when the release time of each job is unknown. In this paper, some online scheduling methods are presented to deal with the tough problem of realtime Just- In-Time manufacturing. In addition to applicable dispatching rules, agent-based approaches are also proposed featuring feedback, learning, and realtime prediction. The simulation result reveals that the presented distributed learning approach, especially when combined with realtime prediction, delivers a high performance.