TY - JOUR
T1 - A Genetics-Based Hybrid Scheduler for Generating Static Schedules in Flexible Manufacturing Contexts
AU - Holsapple, Clyde W.
AU - Jacob, Varghese S.
AU - Pakath, Ramakrishnan
AU - Zaveri, Jigish S.
PY - 1993
Y1 - 1993
N2 - 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.
AB - 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.
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U2 - 10.1109/21.247881
DO - 10.1109/21.247881
M3 - Article
AN - SCOPUS:0027629091
SN - 0018-9472
VL - 23
SP - 953
EP - 972
JO - IEEE Transactions on Systems, Man and Cybernetics
JF - IEEE Transactions on Systems, Man and Cybernetics
IS - 4
ER -