Genetic programming hyper-heuristic for solving dynamic production scheduling problem

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Abednego:2011:ICEEI,
  author =       "Luciana Abednego and Dwi Hendratmo",
  title =        "Genetic programming hyper-heuristic for solving
                 dynamic production scheduling problem",
  booktitle =    "International Conference on Electrical Engineering and
                 Informatics (ICEEI 2011)",
  year =         "2011",
  month =        "17-19 " # jul,
  pages =        "K3--2",
  address =      "Bandung, Indonesia",
  size =         "4 pages",
  abstract =     "This paper investigates the potential use of genetic
                 programming hyper-heuristics for solution of the real
                 single machine production problem. This approach
                 operates on a search space of heuristics rather than
                 directly on a search space of solutions. Genetic
                 programming hyper-heuristics generate new heuristics
                 from a set of potential heuristic components. Real data
                 from production department of a metal industries are
                 used in the experiments. Experimental results show
                 genetic programming hyper-heuristics outperforms other
                 heuristics including MRT, SPT, LPT, EDD, LDD, dan MON
                 rules with respect to minimum tardiness and minimum
                 flow time objectives. Further results on sensitivity to
                 changes indicate that GPHH designs are robust. Based on
                 experiments, GPHH outperforms six other benchmark
                 heuristics with number of generations 50 and number of
                 populations 50. Human designed heuristics are result of
                 years of work by a number of experts, while GPHH
                 automate the design of the heuristics. As the search
                 process is automated, this would largely reduce the
                 cost of having to create a new set of heuristics.",
  keywords =     "genetic algorithms, genetic programming, cost
                 reduction, dynamic production scheduling problem,
                 genetic programming hyper heuristics, metal industries,
                 minimum flow time, minimum tardiness, single machine
                 production problem, cost reduction, dynamic scheduling,
                 heuristic programming, lead time reduction,
                 metallurgical industries, single machine scheduling",
  DOI =          "doi:10.1109/ICEEI.2011.6021768",
  ISSN =         "2155-6822",
  notes =        "Also known as \cite{6021768}",
}

Genetic Programming entries for Luciana Abednego Dwi Hendratmo

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