Processing times estimation in a manufacturing industry through genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Manuel Mucientes and Juan C. Vidal and 
                 Alberto Bugarin and Manuel Lama",
  title =        "Processing times estimation in a manufacturing
                 industry through genetic programming",
  booktitle =    "3rd International Workshop on Genetic and Evolving
                 Fuzzy Systems, GEFS 2008",
  year =         "2008",
  month =        "4-7 " # mar,
  address =      "Witten-Boommerholz, Germany",
  pages =        "95--100",
  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",
  DOI =          "doi:10.1109/GEFS.2008.4484574",
  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.",
  notes =        "Also known as \cite{4484574}",

Genetic Programming entries for Manuel Mucientes Molina Juan Carlos Vidal Aguiar Alberto J Bugarin Diz Manuel Lama Penin