Active rescheduling for automated guided vehicle systems

The paper examines the use of knowledge-based techniques to generate a framework for the active rescheduling of an automated guided vehicle system in a manufacturing environment. The authors´ approach to active rescheduling uses `cues´ drawn from events on the shop-floor to trigger rescheduling. Simulation experiments are used to capture knowledge about the shop-floor and various scheduling strategies. An extensible agent architecture is developed to facilitate active rescheduling