A Coevolution Genetic Programming Method to Evolve Scheduling Policies for Dynamic Multi-objective Job Shop Scheduling Problems

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@InProceedings{Nguyen:2012:CECb,
  title =        "A Coevolution Genetic Programming Method to Evolve
                 Scheduling Policies for Dynamic Multi-objective Job
                 Shop Scheduling Problems",
  author =       "Su Nguyen and Mengjie Zhang and Mark Johnston and 
                 Tan Kay Chen",
  pages =        "3332--3339",
  booktitle =    "Proceedings of the 2012 IEEE Congress on Evolutionary
                 Computation",
  year =         "2012",
  editor =       "Xiaodong Li",
  month =        "10-15 " # jun,
  DOI =          "doi:10.1109/CEC.2012.6252968",
  address =      "Brisbane, Australia",
  ISBN =         "0-7803-8515-2",
  keywords =     "genetic algorithms, genetic programming, Heuristics,
                 metaheuristics and hyper-heuristics, Evolutionary
                 simulation-based optimization",
  abstract =     "A scheduling policy (SP) strongly influences the
                 performance of a manufacturing system. However, the
                 design of an effective SP is complicated and
                 time-consuming due to the complexity of each scheduling
                 decision as well as the interactions between these
                 decisions. This paper proposes novel multi-objective
                 genetic programming based hyper-heuristic methods for
                 automatic design of SPs including dispatching rules
                 (DRs) and due-date assignment rules (DDARs) in job shop
                 environments. The experimental results show that the
                 evolved Pareto front contains effective SPs that can
                 dominate various SPs from combinations of existing DRs
                 with dynamic and regression-based DDARs. The evolved
                 SPs also show promising performance on unseen
                 simulation scenarios with different shop settings. On
                 the other hand, the proposed Diversified
                 Multi-Objective Cooperative Coevolution (DMOCC) method
                 can effectively evolve Pareto fronts of SPs compared to
                 NSGA-II and SPEA2 while the uniformity of SPs obtained
                 by DMOCC is better that those evolved by NSGA-II and
                 SPEA2.",
  notes =        "WCCI 2012. CEC 2012 - A joint meeting of the IEEE, the
                 EPS and the IET.",
}

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

Citations