Automatic Design of Scheduling Policies for Dynamic Multi-objective Job Shop Scheduling via Cooperative Coevolution Genetic Programming

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@Article{Nguyen:2013:ieeeTEC_2,
  author =       "Su Nguyen and Mengjie Zhang and Mark Johnston and 
                 Kay Chen Tan",
  title =        "Automatic Design of Scheduling Policies for Dynamic
                 Multi-objective Job Shop Scheduling via Cooperative
                 Coevolution Genetic Programming",
  journal =      "IEEE Transactions on Evolutionary Computation",
  year =         "2014",
  volume =       "18",
  number =       "2",
  pages =        "193--208",
  month =        apr,
  keywords =     "genetic algorithms, genetic programming, dispatching
                 rule, hyper-heuristic, job shop scheduling",
  ISSN =         "1089-778X",
  DOI =          "doi:10.1109/TEVC.2013.2248159",
  size =         "17 pages",
  abstract =     "A scheduling policy strongly influences the
                 performance of a manufacturing system. However, the
                 design of an effective scheduling policy is complicated
                 and time-consuming due to the complexity of each
                 scheduling decision as well as the interactions among
                 these decisions. This paper develops four new
                 multi-objective genetic programming based
                 hyper-heuristic (MO-GPHH) methods for automatic design
                 of scheduling policies including dispatching rules and
                 due-date assignment rules in job shop environments.
                 Besides using three existing search strategies NSGA-II,
                 SPEA2 and HaD-MOEA to develop new MO-GPHH methods, a
                 new approach called Diversified Multi-Objective
                 Cooperative Coevolution (DMOCC) is also proposed. The
                 novelty of these MO-GPHH methods is that they are able
                 to handle multiple scheduling decisions simultaneously.
                 The experimental results show that the evolved Pareto
                 fronts represent effective scheduling policies that can
                 dominate scheduling policies from combinations of
                 existing dispatching rules with
                 dynamic/regression-based due date assignment rules. The
                 evolved scheduling policies also show dominating
                 performance on unseen simulation scenarios with
                 different shop settings. In addition, the uniformity of
                 the scheduling policies obtained from the proposed
                 method of DMOCC is better than those evolved by other
                 evolutionary approaches.",
  notes =        "also known as \cite{}",
}

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

Citations