Many-Objective Genetic Programming for Job-Shop Scheduling

Created by W.Langdon from gp-bibliography.bib Revision:1.4496

  author =       "Atiya Masood and Yi Mei and Gang Chen2 and 
                 Mengjie Zhang",
  title =        "Many-Objective Genetic Programming for Job-Shop
  booktitle =    "Proceedings of 2016 IEEE Congress on Evolutionary
                 Computation (CEC 2016)",
  year =         "2016",
  editor =       "Yew-Soon Ong",
  pages =        "209--216",
  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.2016.7743797",
  abstract =     "In Job Shop Scheduling (JSS) problems, there are
                 usually many conflicting objectives to consider, such
                 as the makespan, mean flowtime, maximal tardiness,
                 number of tardy jobs, etc. Most studies considered
                 these objectives separately or aggregated them into a
                 single objective (fitness function) and treat the
                 problem as a single-objective optimization. Very few
                 studies attempted to solve the multi-objective JSS with
                 two or three objectives, not to mention the
                 many-objective JSS with more than three objectives. In
                 this paper, we investigate the many-objective JSS,
                 which takes all the objectives into account. On the
                 other hand, dispatching rules have been widely used in
                 JSS due to its flexibility, scalability and quick
                 response in dynamic environment. In this paper, we
                 focus on evolving a set of trade-off dispatching rules
                 for many-objective JSS, which can generate
                 non-dominated schedules given any unseen instance. To
                 this end, a new hybridized algorithm that combines
                 Genetic Programming (GP) and NSGA-III is proposed. The
                 experimental results demonstrates the efficacy of the
                 newly proposed algorithm on the tested job-shop
                 benchmark instances.",
  notes =        "WCCI2016",

Genetic Programming entries for Atiya Masood Yi Mei Gang Chen2 Mengjie Zhang