Combined Use Of Genetic Programming And Decomposition Techniques For The Induction Of Generalized Approximate Throughput Formulas In Short Exponential Production Lines With Buffers

Created by W.Langdon from gp-bibliography.bib Revision:1.7642

@InProceedings{oai:CiteSeerPSU:560410,

• title = "Combined Use Of Genetic Programming And Decomposition Techniques For The Induction Of Generalized Approximate Throughput Formulas In Short Exponential Production Lines With Buffers",
• author = "Chrissoleon Papadopoulos and Athanasios Tsakonas and George Dounias",
• year = "2002",
• booktitle = "Proceedings of the 30th International Conference on Computers \& Industrial Engineering",
• editor = "Chrissoleon Papadopoulos and Evangelos Triantaphyllou",
• address = "Tinos Island, Greece",
• month = "28 " # jun # "-2 " # jul,
• keywords = "genetic algorithms, genetic programming",
• citeseer-isreferencedby = "oai:CiteSeerPSU:94604",
• citeseer-references = "oai:CiteSeerPSU:20226",
• annote = "The Pennsylvania State University CiteSeer Archives",
• language = "en",
• oai = "oai:CiteSeerPSU:560410",
• rights = "unrestricted",
• URL = "http://decision.fme.aegean.gr/members/tsakonas/ICCIE-2002-040401.pdf",
• URL = "http://citeseer.ist.psu.edu/560410.html",
• size = "6 pages",
• abstract = "An attempt is made to combine standard decomposition techniques and genetic programming approaches, for the induction of generalized approximate throughput formulas in short exponential serial production lines with finite intermediate buffers. The domain of serial production lines lacks the existence of general formulas for acquiring useful measurements and line characteristics, such as throughput. Throughput approximation in literature takes place usually with the aid of algorithmic computer-based decomposition techniques. In this paper, decomposition-based data for every different number of stations are used as training cases into a genetic programming scheme, which tries to generalize the calculation of throughput within a single mathematical formula. The proposed formula, obtains accuracy higher than 99% for the training (i.e,. known) data, whereas, it deviates, on average, 5-15% from the accurate decomposed value, for testing (i.e. unknown) production line characteristics.",
• notes = "Two volumes, 1,058 pages long (total) http://www.imse.lsu.edu/vangelis/index.html?http://cda4.imse.lsu.edu/books1/Tinos2002CompsAndIEConference/Tinos2002Proceedings.htm",

}

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

Citations