A Hybrid Evolutionary Hyper-Heuristic Approach for Intercell Scheduling Considering Transportation Capacity

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@Article{Li:2016:ieeeASE,
  author =       "Dongni Li and Rongxin Zhan and Dan Zheng and 
                 Miao Li and Ikou Kaku",
  journal =      "IEEE Transactions on Automation Science and
                 Engineering",
  title =        "A Hybrid Evolutionary Hyper-Heuristic Approach for
                 Intercell Scheduling Considering Transportation
                 Capacity",
  year =         "2016",
  volume =       "13",
  number =       "2",
  pages =        "1072--1089",
  abstract =     "The problem of intercell scheduling considering
                 transportation capacity with the objective of
                 minimizing total weighted tardiness is addressed in
                 this paper, which in nature is the coordination of
                 production and transportation. Since it is a practical
                 decision-making problem with high complexity and large
                 problem instances, a hybrid evolutionary
                 hyper-heuristic (HEH) approach, which combines
                 heuristic generation and heuristic selection, is
                 developed in this paper. In order to increase the
                 diversity and effectiveness of heuristic rules, genetic
                 programming is used to automatically generate new rules
                 based on the attributes of parts, machines, and
                 vehicles. The new rules are added to the candidate rule
                 set, and a rule selection genetic algorithm is
                 developed to choose appropriate rules for machines and
                 vehicles. Finally, scheduling solutions are obtained
                 using the selected rules. A comparative evaluation is
                 conducted, with some state-of-the-art hyper-heuristic
                 approaches which lack some of the strategies proposed
                 in HEH, with a meta-heuristic approach that is suitable
                 for large scale scheduling problems, and with
                 adaptations of some well-known heuristic rules.
                 Computational results show that the new rules generated
                 in HEH have similarities to the best-performing
                 human-made rules, but are more effective due to the
                 evolutionary processes in HEH. Moreover, the HEH
                 approach has advantages over other approaches in both
                 computational efficiency and solution quality, and is
                 especially suitable for problems with large instance
                 sizes.",
  keywords =     "genetic algorithms, genetic programming, Job shop
                 scheduling, Processor scheduling, Search problems,
                 Vehicles, Discrete event systems, manufacturing
                 automation, scheduling, transportation",
  DOI =          "doi:10.1109/TASE.2015.2470080",
  ISSN =         "1545-5955",
  month =        apr,
  notes =        "Also known as \cite{7270346}",
}

Genetic Programming entries for Dongni Li Rongxin Zhan Dan Zheng Miao Li Ikou Kaku

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