Genetic Programming for Evolving Due-date Assignment Models in Job Shop Environments

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "Su Nguyen and Mengjie Zhang and Mark Johnston and 
                 Kay Chen Tan",
  title =        "Genetic Programming for Evolving Due-date Assignment
                 Models in Job Shop Environments",
  journal =      "Evolutionary Computation",
  year =         "2014",
  volume =       "22",
  number =       "1",
  pages =        "105--138",
  month =        "Spring",
  keywords =     "genetic algorithms, genetic programming, job shop,
                 due-date assignment, hyper-heuristics",
  ISSN =         "1063-6560",
  URL =          "",
  DOI =          "doi:10.1162/EVCO_a_00105",
  size =         "27 pages",
  abstract =     "Due-date assignment plays an important role in
                 scheduling systems and strongly influences the delivery
                 performance of job shops. Because of the stochastic and
                 dynamic nature of job shops, the development of general
                 due-date assignment models (DDAMs) is complicated. In
                 this study, two genetic programming (GP) methods are
                 proposed to evolve DDAMs for job shop environments. The
                 experimental results show that the evolved DDAMs can
                 make more accurate estimates than other existing
                 dynamic DDAMs with promising reusability. In addition,
                 the evolved operation-based DDAMs show better
                 performance than the evolved DDAMs employing aggregate
                 information of jobs and machines.",

Genetic Programming entries for Su Nguyen Mengjie Zhang Mark Johnston Kay Chen Tan