Calculation of throughput for production lines with buffers using computational intelligence
Created by W.Langdon from
gpbibliography.bib Revision:1.7831
 @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 = "1115",

address = "Vinnitsa State Technical University, Ukraine",

publisher_address = "Ukraine",

month = oct # " 812",

publisher = "UNIVERSUMVinnitsa, BG",

keywords = "genetic algorithms, genetic programming, computational
intelligence, decomposition techniques, symbolic
regression, throughput",

ISBN = "9666410397",

URL = "http://mdelab.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 highlevel 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? KYCC2001
http://catalog.odnb.odessa.ua/opac/index.php?url=/notices/index/IdNotice:12348/Source:default
http://mdelab.aegean.gr/researchmaterial",
 }
Genetic Programming entries for
Athanasios D Tsakonas
Chrissoleon T Papadopoulos
Georgios Dounias
Citations