Generation of Robust Recovery Logic in Assembly Systems using Multi-Level Optimization and Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{oai:CiteSeerPSU:535775,
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author = "Cem M Baydar and Kazuhiro Saitou",
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title = "Generation of Robust Recovery Logic in Assembly
Systems using Multi-Level Optimization and Genetic
Programming",
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booktitle = "Proceedings of DETC-00 ASME 2000 Design Engineering
Technical Conferences and Computers and Information in
Engineering Conference",
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year = "2000",
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address = "Baltimore, Maryland, USA",
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month = "10-13 " # sep,
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keywords = "genetic algorithms, genetic programming",
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citeseer-isreferencedby = "oai:CiteSeerPSU:87724;
oai:CiteSeerPSU:467824; oai:CiteSeerPSU:161643",
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annote = "The Pennsylvania State University CiteSeer Archives",
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language = "en",
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oai = "oai:CiteSeerPSU:535775",
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rights = "unrestricted",
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URL = "http://citeseer.ist.psu.edu/535775.html",
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size = "8 pages",
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abstract = "Automated assembly lines are subject to unexpected
failures, which can cause costly shutdowns. Generally,
these errors are handled by human experts or logic
controllers. However, these controller codes are based
on anticipated error scenarios and are deficient in
dealing with unforeseen situations. In our previous
work (Baydar and Saitou, 2000a), an approach for the
automated generation of error recovery logic was
discussed. The method is based on three-dimensional
geometric modeling of the assembly line to generate
error recovery logic in an {"}off-line{"} manner using
Genetic Programming. The scope of our previous work was
focused on finding an error recovery algorithm from a
predefined error case. However due to the geometrical
features of the assembly lines, there may be cases
which can be detected as the same type of error by the
sensors. Therefore robustness must be assured in the
sense of having a common recovery algorithm for similar
cases during the recovery sequence. In this paper, an
extension of our previous study is presented to
overcome this problera An assembly line is modeled and
from the given error cases optimum way of error
recovery is investigated using multi-level
optimization. The obtained results showed that the
infrastructure is capable of finding robust error
recovery algorithms and multi-level optimization
procedure improved the process. It is expected that the
results of this study will be combined with the
automatic error generation, resulting in efficient ways
to automated error recovery logic synthesis.",
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notes = "not verified",
- }
Genetic Programming entries for
Cem M Baydar
Kazuhiro Saitou
Citations