Automatic Programming via Iterated Local Search for Dynamic Job Shop Scheduling

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@Article{Nguyen:2014:ieeeCybernetics,
  author =       "Su Nguyen and Mengjie Zhang and Mark Johnston and 
                 Kay Chen Tan",
  title =        "Automatic Programming via Iterated Local Search for
                 Dynamic Job Shop Scheduling",
  journal =      "IEEE Transactions on Cybernetics",
  year =         "2015",
  volume =       "45",
  number =       "1",
  month =        jan,
  pages =        "1--14",
  keywords =     "genetic algorithms, genetic programming, Dynamic job
                 shop scheduling, hyper-heuristic, scheduling rule",
  DOI =          "doi:10.1109/TCYB.2014.2317488",
  ISSN =         "2168-2267",
  abstract =     "Dispatching rules have been commonly used in practice
                 for making sequencing and scheduling decisions. Due to
                 specific characteristics of each manufacturing system,
                 there is no universal dispatching rule that can
                 dominate in all situations. Therefore, it is important
                 to design specialised dispatching rules to enhance the
                 scheduling performance for each manufacturing
                 environment. Evolutionary computation approaches such
                 as tree-based genetic programming (TGP) and gene
                 expression programming (GEP) have been proposed to
                 facilitate the design task through automatic design of
                 dispatching rules. However, these methods are still
                 limited by their high computational cost and low
                 exploitation ability. To overcome this problem, we
                 develop a new approach to automatic programming via
                 iterated local search (APRILS) for dynamic job shop
                 scheduling. The key idea of APRILS is to perform
                 multiple local searches started with programs modified
                 from the best obtained programs so far. The experiments
                 show that APRILS outperforms TGP and GEP in most
                 simulation scenarios in terms of effectiveness and
                 efficiency. The analysis also shows that programs
                 generated by APRILS are more compact than those
                 obtained by genetic programming. An investigation of
                 the behaviour of APRILS suggests that the good
                 performance of APRILS comes from the balance between
                 exploration and exploitation in its search mechanism.",
  notes =        "Also known as \cite{6807725}",
}

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

Citations