Processing times estimation in a manufacturing industry through genetic programming
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- @InProceedings{Mucientes:2008:GEFS,
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author = "Manuel Mucientes and Juan C. Vidal and
Alberto Bugarin and Manuel Lama",
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title = "Processing times estimation in a manufacturing
industry through genetic programming",
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booktitle = "3rd International Workshop on Genetic and Evolving
Fuzzy Systems, GEFS 2008",
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year = "2008",
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month = "4-7 " # mar,
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address = "Witten-Boommerholz, Germany",
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pages = "95--100",
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keywords = "genetic algorithms, genetic programming,
Takagi-Sugeno-Kang fuzzy rule-based system,
context-free grammar, knowledge structure,
manufacturing industry, processing time estimation,
production plan, regression function, wood furniture
industry, context-free grammars, estimation theory,
furniture industry, fuzzy reasoning, fuzzy set theory,
knowledge based systems, production planning,
regression analysis, wood",
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DOI = "doi:10.1109/GEFS.2008.4484574",
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abstract = "Accuracy in processing time estimation of
manufacturing operations is fundamental to achieve more
competitive prices and higher profits in an industry.
The manufacturing times of a machine depend on several
input variables and, for each class or type of product,
a regression function for that machine can be defined.
Time estimations are used for implementing production
plans. These plans are usually supervised and modified
by an expert, so information about the dependencies of
processing time with the input variables is also very
important. Taking into account both premises (accuracy
and simplicity in information extraction), a model
based on TSK (Takagi-Sugeno-Kang) fuzzy rules has been
used. TSK rules fulfill both requisites: the system has
a high accuracy, and the knowledge structure makes
explicit the dependencies between time estimations and
the input variables. We propose a TSK fuzzy rule model
in which the rules have a variable structure in the
consequent, as the regression functions can be
completely distinct for different machines or, even,
for different classes of inputs to the same machine.
The methodology to learn the TSK knowledge base is
based on genetic programming together with a
context-free grammar to restrict the valid structures
of the regression functions. The system has been tested
with real data coming from five different machines of a
wood furniture industry.",
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notes = "Also known as \cite{4484574}",
- }
Genetic Programming entries for
Manuel Mucientes Molina
Juan Carlos Vidal Aguiar
Alberto J Bugarin Diz
Manuel Lama Penin
Citations