Generation of Robust Recovery Logic in Assembly Systems using Multi-Level Optimization and Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4504

  author =       "Cem M Baydar and Kazuhiro Saitou",
  title =        "Generation of Robust Recovery Logic in Assembly
                 Systems using Multi-Level Optimization and Genetic
  booktitle =    "Proceedings of DETC-00 ASME 2000 Design Engineering
                 Technical Conferences and Computers and Information in
                 Engineering Conference",
  year =         "2000",
  address =      "Baltimore, Maryland, USA",
  month =        "10-13 " # sep,
  keywords =     "genetic algorithms, genetic programming",
  citeseer-isreferencedby = "oai:CiteSeerPSU:87724;
                 oai:CiteSeerPSU:467824; oai:CiteSeerPSU:161643",
  annote =       "The Pennsylvania State University CiteSeer Archives",
  language =     "en",
  oai =          "oai:CiteSeerPSU:535775",
  rights =       "unrestricted",
  URL =          "",
  size =         "8 pages",
  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.",
  notes =        "not verified",

Genetic Programming entries for Cem M Baydar Kazuhiro Saitou