Reducing myopic behavior in FMS control: A semi-heterarchical simulation-optimization approach

Heterarchical FMS control architectures localize decisional capabilities in each entity, resulting in highly reactive, low complexity control architectures. Unfortunately, these architectures present myopic behavior since decisional entities have limited visibility of other decisional entities' behavior and the alignment of an entity's decision with the system's global objective. In this paper, we propose a semi-heterarchical architecture in which a supervisor tackles different kinds of myopic decisions using simulation-optimization mechanisms and the current conditions of a flexible manufacturing system (FMS). The supervisor uses simulation results to calculate local and global performances and to evolve the solutions proposed by the optimization mechanisms. The approach proposed was configured to control a real assembly cell with highly heterarchical approaches. The completion time variance was used as the performance measure for myopic behavior reduction. The simulation results showed that the semi-heterarchical architecture can reduce myopic behavior whereby it strikes a balance between the ability to react to disturbances and maintaining low complexity, thus making it suitable for production control.