A Comprehensive Analysis on Reusability of GP-Evolved Job Shop Dispatching Rules

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

@InProceedings{Mei:2016:CEC,
  author =       "Yi Mei and Mengjie Zhang",
  title =        "A Comprehensive Analysis on Reusability of GP-Evolved
                 Job Shop Dispatching Rules",
  booktitle =    "Proceedings of 2016 IEEE Congress on Evolutionary
                 Computation (CEC 2016)",
  year =         "2016",
  editor =       "Yew-Soon Ong",
  pages =        "3590--3597",
  address =      "Vancouver",
  month =        "24-29 " # jul,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-1-5090-0623-6",
  DOI =          "doi:10.1109/CEC.2016.7744244",
  abstract =     "Genetic Programming (GP) has been extensively used to
                 automatically design dispatching rules for job shop
                 scheduling problems. However, the previous studies only
                 focus on the performance on the training instances. So
                 far, there is no systematic investigation of the
                 reusability of the GP-evolved rules on unseen
                 instances. In practice, it is desirable to train the
                 rules on smaller job shop instances, and apply them to
                 larger instances with more jobs and machines to save
                 training time. In this case, the reusability of the
                 GP-evolved rules under different numbers of jobs and
                 machines is an important issue. In this paper, a
                 comprehensive investigation is conducted to analyse how
                 the variation in the numbers of jobs and machines from
                 the training set to the test set affects the
                 reusability of the GP-evolved rules. It is found that
                 in terms of minimizing makespan, the reusability of the
                 GP-evolved rules highly depends on variation in the
                 numbers of jobs and machines. A better reusability can
                 be achieved by choosing training instances whose
                 numbers of jobs and machines (or at least the ratio
                 between the numbers of jobs and machines) are closer to
                 that of the test instances. Furthermore, the ratio
                 between the numbers of jobs and machines is
                 demonstrated to be an important factor to reflect the
                 complexity of an instance for dispatching rules. This
                 study is the first systematic investigation on the
                 reusability of GP-evolved dispatching rules.",
  notes =        "WCCI2016",
}

Genetic Programming entries for Yi Mei Mengjie Zhang

Citations