Intelligent Scheduling with Machine Learning Capabilities: The Induction of Scheduling Knowledge

Dynamic scheduling of manufacturing systems has primarily involved the use of dispatching rules. In the context of conventional job shops, the relative performance of these rules has been found to depend upon the system attributes, and no single rule is dominant across all possible scenarios. This indicates the need for developing a scheduling approach which adopts a state-dependent dispatching rule selection policy. The importance of adapting the dispatching rule employed to the current state of the system is even more critical in a flexible manufacturing system because of alternative machine routing possibilities and the need for increased coordination among various machines. This study develops a framework for incorporating machine learning capabilities in intelligent scheduling. A pattern-directed method, with a built-in inductive learning module, is developed for heuristic acquisition and refinement. This method enables the scheduler to classify distinct manufacturing patterns and to generate a decision tree consisting of heuristic policies for dynamically selecting the dispatching rule appropriate for a given set of system attributes. Computational experience indicates that the learning-augmented approach leads to improved system performance. In addition, the process of generating the decision tree shows the efficacy of inductive learning in extracting and ranking the various system attributes relevant for deciding upon the appropriate dispatching rule to employ.
Publisher
Taylor & Francis Inc
Issue Date
1992-05
Language
ENG
Keywords

EXPERT SYSTEMS

Citation

IIE TRANSACTIONS, v.24, no.2, pp.156 - 168

ISSN
0740-817X
DOI
10.1080/07408179208964213
URI
http://hdl.handle.net/10203/59946
Appears in Collection
NE-Journal Papers(저널논문)
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