Optimization of flexible process planning by genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Article{li:2008:IJAMT,

• author = "X. Y. Li and X. Y. Shao and L. Gao",
• title = "Optimization of flexible process planning by genetic programming",
• journal = "The International Journal of Advanced Manufacturing Technology",
• year = "2008",
• volume = "38",
• number = "1-2",
• pages = "143--153",
• month = jul,
• keywords = "genetic algorithms, genetic programming, flexible process planning, Process plans selection, Optimization",
• ISSN = "0268-3768",
• DOI = "doi:10.1007/s00170-007-1069-x",
• size = "11 pages",
• 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.",
• 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

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