An Artificial Intelligence Approach to the Scheduling of Flexible Manufacturing Systems

Abstract

Scheduling in a flexible manufacturing system (FMS)must take into account the shorter lead-time, the multiprocessing environment, the flexibility of machine tools, and the dynamically changing states. The scheduling approach described in this paper employs a knowledge-based system to carry out the nonlinear planning method developed in artificial intelligence. The state-space process for plan-generation, by either forward- or backward-chaining, can handle scheduling requirements unique to the FMS environment. A prototype of this scheduling system has been implemented on a LISP machine and is applied to solve the scheduling problem in flexible manufacturing cells. This scheduling method is characterized by its knowledge-based organization, symbolic representation, state-space inferencing, and its ability for dynamic scheduling and plan revision. It provides a foundation for integrating intelligent planning, scheduling, and machine learning in FMSs.