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@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