Genetic Programming Hyper-heuristic with Cooperative Coevolution for Dynamic Flexible Job Shop Scheduling

Created by W.Langdon from gp-bibliography.bib Revision:1.4163

@InProceedings{Yska:2018:EuroGP,
  author =       "Daniel Yska and Yi Mei and Mengjie Zhang",
  title =        "Genetic Programming Hyper-heuristic with Cooperative
                 Coevolution for Dynamic Flexible Job Shop Scheduling",
  booktitle =    "EuroGP 2018: Proceedings of the 21st European
                 Conference on Genetic Programming",
  year =         "2018",
  month =        "4-6 " # apr,
  editor =       "Mauro Castelli and Lukas Sekanina and 
                 Mengjie Zhang and Stefano Cagnoni and Pablo Garcia-Sanchez",
  series =       "LNCS",
  volume =       "10781",
  publisher =    "Springer Verlag",
  address =      "Parma, Italy",
  pages =        "306--321",
  organisation = "EvoStar, Species",
  keywords =     "genetic algorithms, genetic programming: Poster",
  isbn13 =       "978-3-319-77552-4",
  URL =          "http://homepages.ecs.vuw.ac.nz/~yimei/papers/EuroGP18-Daniel.pdf",
  DOI =          "doi:10.1007/978-3-319-77553-1_19",
  abstract =     "Flexible Job Shop Scheduling (FJSS) problem has many
                 real-world applications such as manufacturing and cloud
                 computing, and thus is an important area of study. In
                 real world, the environment is often dynamic, and
                 unpredicted job orders can arrive in real time. Dynamic
                 FJSS consists of challenges of both dynamic
                 optimisation and the FJSS problem. In Dynamic FJSS, two
                 kinds of decisions (so-called routing and sequencing
                 decisions) are to be made in real time. Dispatching
                 rules have been demonstrated to be effective for
                 dynamic scheduling due to their low computational
                 complexity and ability to make real-time decisions.
                 However, it is time consuming and strenuous to design
                 effective dispatching rules manually due to the complex
                 interactions between job shop attributes. Genetic
                 Programming Hyper-heuristic (GPHH) has shown success in
                 automatically designing dispatching rules which are
                 much better than the manually designed ones. Previous
                 works only focused on standard job shop scheduling with
                 only the sequencing decisions. For FJSS, the routing
                 rule is set arbitrarily by intuition. In this paper, we
                 explore the possibility of evolving both routing and
                 sequencing rules together and propose a new GPHH
                 algorithm with Cooperative Co-evolution. Our results
                 show that co-evolving the two rules together can lead
                 to much more promising results than evolving the
                 sequencing rule only.",
  notes =        "Part of \cite{Castelli:2018:GP} EuroGP'2018 held in
                 conjunction with EvoCOP2018, EvoMusArt2018 and
                 EvoApplications2018",
}

Genetic Programming entries for Daniel Yska Yi Mei Mengjie Zhang

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