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A genetics-based hybrid scheduler for generating static schedules in flexible manufacturing contexts

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4 Author(s)
Holsapple, C.W. ; Dept. of Decision Sci. & Inf. Syst., Kentucky Univ., Lexington, KY, USA ; Jacob, V.S. ; Pakath, R. ; Zaveri, J.S.

Existing computerized systems that support scheduling decisions for flexible manufacturing systems (FMS's) rely largely on knowledge acquired through rote learning 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 in-depth investigations

Published in:

Systems, Man and Cybernetics, IEEE Transactions on  (Volume:23 ,  Issue: 4 )

Date of Publication:

Jul/Aug 1993

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