A Genetics-Based Hybrid Scheduler for Generating Static Schedules in Flexible Manufacturing Contexts

Clyde W. Holsapple, Varghese S. Jacob, Ramakrishnan Pakath, Jigish S. Zaveri

Research output: Contribution to journalArticlepeer-review

73 Scopus citations

Abstract

Existing computerized systems that support scheduling decisions for flexible manufacturing systems (FMS's) rely largely on knowledge acquired through rote learning (i.e., memorization) for schedule generation. In a few instances, the systems also possess some ability to learn using deduction or supervised induction. We introduce a novel AI-based system for generating static schedules that makes heavy use of an unsupervised learning module in acquiring significant portions of the requisite problem processing knowledge. This scheduler pursues a hybrid schedule generation strategy wherein it effectively combines knowledge acquired via genetics-based unsupervised induction with rote-learned knowledge in generating high-quality schedules in an efficient manner. Through a series of experiments conducted on a randomly generated problem of practical complexity, we show that the hybrid scheduler strategy is viable, promising, and, worthy of more iNDepth investigations.

Original languageEnglish
Pages (from-to)953-972
Number of pages20
JournalIEEE Transactions on Systems, Man and Cybernetics
Volume23
Issue number4
DOIs
StatePublished - 1993

ASJC Scopus subject areas

  • General Engineering

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