Integrating inductive learning and simulation in rule-based scheduling

Sang Chan Park, Selwyn Piramuthu, Narayan Raman, Michael J. Shaw

Research output: Chapter in Book/Report/Conference proceedingConference contribution


This paper proposes a framework for incorporating machine learning into the real time scheduling of a flexible manufacturing system, and extends it to scheduling in a flexible flow system. While the bulk of previous research on dynamic machine scheduling deals with the relative effectiveness of a single scheduling rule, the approach presented in this study provides a mechanism for the state-dependent selection of one from among several rules. We develop a Pattern Directed Scheduler (PDS) with a built-in inductive learning module for heuristic acquisition and refinement. Both simulation and inductive learning modules complement each other, resulting in improvement in the overall performance of the system. Computational results show that such a pattern directed scheduling results in favorable scheduling performance, intelligent scheduling mechanism.

Original languageEnglish (US)
Title of host publicationExpert Systems in Engineering
Subtitle of host publicationPrinciples and Applications - International Workshop, Proceedings
EditorsGeorg Gottlob, Wolfgang Nejdl
Number of pages16
ISBN (Print)9783540467113
StatePublished - 1990
EventInternational Workshop on Expert Systems in Engineering, 1990 - Vienna, Austria
Duration: Sep 24 1990Sep 26 1990

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume462 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


OtherInternational Workshop on Expert Systems in Engineering, 1990

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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