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Intelligent dynamic scheduling system: the application of genetic algorithms

Intelligent dynamic scheduling system: the application of genetic algorithms Learning machine scheduling strategies are addressed while concentrating on the dynamic nature of real systems. A framework is proposed consisting of two modules: intelligent simulation (IS) and incremental learning. A simulation technique is basically exploited to mirror the manufacturing system. The knowledge base incorporated within the simulation environment enables the IS to behave intelligently as well as to evaluate the knowledge base (KB). A genetic algorithm drives the learning module. Its ingredients are tailored to tackle such a problem with a huge search space. A set of decision rules is identified as a chromosome. The rule set's fitness is related to the scheduling performance measure and is scaled. A crossover and three kinds of mutations together with a steady-state replacement technique are designed to discover the (near) best rule set. The whole framework is designed to work in an automated way. A series of test results on a basic model show that the proposed system learns, adapts itself to the dominating dynamic patterns, and converges to the optimum solution. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Integrated Manufacturing Systems Emerald Publishing

Intelligent dynamic scheduling system: the application of genetic algorithms

M. Jahangirian; G.V. Conroy
Integrated Manufacturing Systems , Volume 11 (4): 11 – Jul 1, 2000
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References (35)

Publisher
Emerald Publishing
Copyright
Copyright © 2000 MCB UP Ltd. All rights reserved.
ISSN
0957-6061
DOI
10.1108/09576060010326375
Publisher site
See Article on Publisher Site

Abstract

Learning machine scheduling strategies are addressed while concentrating on the dynamic nature of real systems. A framework is proposed consisting of two modules: intelligent simulation (IS) and incremental learning. A simulation technique is basically exploited to mirror the manufacturing system. The knowledge base incorporated within the simulation environment enables the IS to behave intelligently as well as to evaluate the knowledge base (KB). A genetic algorithm drives the learning module. Its ingredients are tailored to tackle such a problem with a huge search space. A set of decision rules is identified as a chromosome. The rule set's fitness is related to the scheduling performance measure and is scaled. A crossover and three kinds of mutations together with a steady-state replacement technique are designed to discover the (near) best rule set. The whole framework is designed to work in an automated way. A series of test results on a basic model show that the proposed system learns, adapts itself to the dominating dynamic patterns, and converges to the optimum solution.

Journal

Integrated Manufacturing Systems – Emerald Publishing

Published: Jul 1, 2000

Keywords: Algorithms; Simulation

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