On the automatic discovery of variants of the NEH procedure for flow shop scheduling using genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Article{Vazquez-Rodriguez:2011:JORS,

• author = "Jose Antonio Vazquez Rodriguez and Gabriela Ochoa",
• title = "On the automatic discovery of variants of the {NEH} procedure for flow shop scheduling using genetic programming",
• journal = "Journal of the Operational Research Society",
• year = "2011",
• number = "2",
• volume = "62",
• pages = "381--396",
• keywords = "genetic algorithms, genetic programming, heuristics, production, hyper-heuristics",
• ISSN = "0160-5682",
• URL = "http://www.cs.stir.ac.uk/~goc/papers/NEHGP_JORS.pdf",
• URL = "http://www.palgrave-journals.com/jors/journal/v62/n2/full/jors2010132a.html",
• DOI = "doi:10.1057/jors.2010.132",
• URL = "http://results.ref.ac.uk/Submissions/Output/944105",
• size = "16 pages",
• abstract = "We use genetic programming to find variants of the well-known Nawaz, En-score and Ham (NEH) heuristic for the permutation flow shop problem. Each variant uses a different ranking function to prioritise operations during schedule construction. We have tested our ideas on problems where jobs have release times, due dates, and weights and have considered five objective functions: makespan, sum of tardiness, sum of weighted tardiness, sum of completion times and sum of weighted completion times. The implemented genetic programming system has been carefully tuned and used to generate one variant of NEH for each objective function. The new NEHs, obtained with genetic programming, have been compared with the original NEH and randomised NEH versions on a large set of benchmark problems. Our results indicate that the NEH variants discovered by genetic programming are superior to the original NEH and its stochastic version on most of the problems investigated.",
• bibdate = "2011-01-25",
• bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/jors/jors62.html#RodriguezO11",
• uk_research_excellence_2014 = "This paper presents an automatic genetic programming approach to design specialised variants of the most successful constructive heuristic for the well studied flow shop scheduling problem. The proposed methodology significantly outperforms the original heuristic on the benchmarks studied. Once a variant of the heuristic, targeted to a class of instances, is discovered, it can be applied to quickly solve a new instance. The exploration of genetic programming to generate new heuristics plays a key role in a new major EPSRC programme grant (EP/J017515/1) of pounds6.8M between UCL, Stirling, York and Birmingham, which started in 2012.",

}

Genetic Programming entries for Jose Antonio Vazquez Rodriguez Gabriela Ochoa

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