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Extending robust industrial optimisation using simulation and reinforcement learning

Creighton, Douglas. 2004, Extending robust industrial optimisation using simulation and reinforcement learning, Ph.D. thesis, School of Engineering and Technology, Deakin University.

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Title Extending robust industrial optimisation using simulation and reinforcement learning
Author Creighton, Douglas.
Institution Deakin University
School School of Engineering and Technology
Faculty Faculty of Science and Technology
Degree name Ph.D.
Date submitted 2004
Keyword(s) Production control - Automation
Computer integrated manufacturing systems
Intelligent agents (Computer software)
Reinforcement learning (Machine learning)
Summary Traditional optimisation methods are incapable of capturing the complexity of today's dynamic manufacturing systems. A new methodology, integrating simulation models and intelligent learning agents, was successfully applied to identify solutions to a fundamental scheduling problem. The robustness of this approach was then demonstrated through a series of real-world industrial applications.
Language eng
Description of original xvi, 223 leaves : ill. ; 30 cm.
Dewey Decimal Classification 670.427
Persistent URL http://hdl.handle.net/10536/DRO/DU:30026784

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