Calculation of throughput for production lines with buffers using computational intelligence

Created by W.Langdon from gp-bibliography.bib Revision:1.4496

@InProceedings{tsakonas_throughput_2001,
  author =       "A. Tsakonas and C. Papadopoulos and G. Dounias",
  title =        "Calculation of throughput for production lines with
                 buffers using computational intelligence",
  booktitle =    "The Sixth International Conference on Measurement and
                 Control in Complex Systems, MCCS 2001",
  year =         "2001",
  editor =       "V. M. Dubova",
  pages =        "11--15",
  address =      "Vinnitsa State Technical University, Ukraine",
  publisher_address = "Ukraine",
  month =        oct # " 8-12",
  publisher =    "UNIVERSUM-Vinnitsa, BG",
  keywords =     "genetic algorithms, genetic programming, computational
                 intelligence, decomposition techniques, symbolic
                 regression, throughput",
  ISBN =         "966-641-039-7",
  URL =          "http://mde-lab.aegean.gr/images/stories/docs/CC21.pdf",
  size =         "5 pages",
  abstract =     "The domain of serial production lines lacks the
                 existence of general formulas for acquiring useful
                 measurements and line characteristics such as
                 throughput. Throughput is called the average number of
                 jobs per hour that can flow through a production line.
                 The obvious complexity of the domain due to
                 combinatorial explosion depends on the number of
                 workstations involved in the examined line the capacity
                 of buffers existing within the workstations the
                 variability in processing times etc. The authors
                 attempt to approximate this problem by applying modern
                 genetic programming techniques [Koza 1992] [Koza 1994]
                 [Angeline et. al 1996] in other words creative
                 programming techniques that belong to the area of
                 computational intelligence and learning. Genetic
                 programming is an automated method for creating a
                 working computer program from a high-level problem
                 statement o f the problem. The evolutionary search
                 adopted uses the Darwinian principle of survival of the
                 fittest and is patterned after naturally occurring
                 operations including crossover (i.e. sexual
                 recombination) mutation gene duplication gene deletion
                 etc. The objective of this work is to obtain an
                 analytical formula for throughput x in terms of the
                 above mentioned production line parameters (i.e. of the
                 number of stations size of buffers mean processing
                 time) assuming there are sufficient jobs at the
                 beginning of the line to ensure that the first station
                 is never starved of jobs and that the last station is
                 never blocked. Through this paper different formulas
                 are given for each size of short production lines with
                 respect to their line length and then an additional
                 attempt is described and analysed for unifying all the
                 throughput formulas obtained during the initial
                 approach. The formulas obtained are quite long but
                 easily programmable in a single line of source code and
                 thus very useful for immediate use in real world
                 applications.",
  notes =        "The throughput rate of short exponential production
                 lines with finite intermediate buffers using genetic
                 programming approximation techniques

                 broken 2018 http://www.vstu.vinnica.ua/mccs2001
                 Published 2002? KYCC-2001
                 http://catalog.odnb.odessa.ua/opac/index.php?url=/notices/index/IdNotice:12348/Source:default

                 http://mde-lab.aegean.gr/research-material",
}

Genetic Programming entries for Athanasios D Tsakonas Chrissoleon T Papadopoulos Georgios Dounias

Citations