Optimization of flexible process planning by genetic programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @Article{li:2008:IJAMT,
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author = "X. Y. Li and X. Y. Shao and L. Gao",
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title = "Optimization of flexible process planning by genetic
programming",
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journal = "The International Journal of Advanced Manufacturing
Technology",
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year = "2008",
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volume = "38",
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number = "1-2",
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pages = "143--153",
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month = jul,
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keywords = "genetic algorithms, genetic programming, flexible
process planning, Process plans selection,
Optimization",
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ISSN = "0268-3768",
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DOI = "doi:10.1007/s00170-007-1069-x",
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size = "11 pages",
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abstract = "The traditional manufacturing system research
literature generally assumed that there was only one
feasible process plan for each job. This implied that
there was no flexibility considered in the process
plan. But, in the modern manufacturing system, most
jobs may have a large number of flexible process plans.
So, flexible process plans selection in a manufacturing
environment has become a crucial problem. In this
paper, a new method using an evolutionary algorithm,
called genetic programming (GP), is presented to
optimize flexible process planning. The flexible
process plans and the mathematical model of flexible
process planning have been described, and a network
representation is adopted to describe the flexibility
of process plans. To satisfy GP, it is very important
to convert the network to a tree. The efficient genetic
representations and operator schemes also have been
considered. Case studies have been used to test the
algorithm, and the comparison has been made for this
approach and genetic algorithm (GA), which is another
popular evolutionary approach to indicate the
adaptability and superiority of the GP-based approach.
The experimental results show that the proposed method
is promising and very effective in the optimization
research of flexible process planning.",
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notes = "The State Key Laboratory of Digital Manufacturing
Equipment and Technology, Huazhong University of
Science and Technology, Wuhan, Hubei, China",
- }
Genetic Programming entries for
Xinyu Li
Xinyu Shao
Liang Gao
Citations