Candidate Rule Selection to Develop Intelligent Scheduling Aids for Flexible Manufacturing Systems (FMS) View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

1993

AUTHORS

Yuehwern Yih , Albert T. Jones

ABSTRACT

In this paper we propose an intelligent, real-time, computer-based decision aid to effectively schedule jobs through a flexible manufacturing system (FMS). One way to develop such an aid is to use a simulation technique to collect information about the impact of heuristic rules on the system performance. The resulting information forms the basis of an inductive learning process for choosing a given rule in a given situation. Since there are more than 100 commonly used heuristic rules, this is an impractical approach for analyzing all the rules and their combinations. This paper advocates a three step systematic approach to real-time scheduling: quick analysis to select a small number of candidate heuristics, followed by a more thorough analysis of those candidates to generate a schedule, and the development of an automated scheduling system. In this paper we concentrate on steps one and three. We utilize neural networks for candidate rule selector and trace-driven knowledge acquisition (TDKA) for developing the automated scheduling system. An example of training data for the rule selection network is given. Also, an example is provided to demonstrate the learning process through TDKA. More... »

PAGES

201-217

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-642-78063-9_13

DOI

http://dx.doi.org/10.1007/978-3-642-78063-9_13

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1046070571


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