Active rescheduling for automated guided vehicle systems

Abstract

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. Our 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.