An artificial intelligence approach to the scheduling of flexible manufacturing systems

Michael J. Shaw, Andrew B. Whinston

Research output: Contribution to journalArticlepeer-review

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 backwardchaining, can handle scheduling requirements unique to the FMS environment. A prototype of this scheduling system has been implemented on a USP 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.

Original languageEnglish (US)
Pages (from-to)170-183
Number of pages14
JournalIIE Transactions (Institute of Industrial Engineers)
Volume21
Issue number2
DOIs
StatePublished - Jun 1989

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering

Fingerprint Dive into the research topics of 'An artificial intelligence approach to the scheduling of flexible manufacturing systems'. Together they form a unique fingerprint.

Cite this