A Sequential Genetic Programming Method to Learn Forward Construction Heuristics for Order Acceptance and Scheduling

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

@InProceedings{Nguyen:2014:CECb,
  title =        "A Sequential Genetic Programming Method to Learn
                 Forward Construction Heuristics for Order Acceptance
                 and Scheduling",
  author =       "Su Nguyen and Mengjie Zhang and Mark Johnston",
  pages =        "1824--1831",
  booktitle =    "Proceedings of the 2014 IEEE Congress on Evolutionary
                 Computation",
  year =         "2014",
  month =        "6-11 " # jul,
  editor =       "Carlos A. {Coello Coello}",
  address =      "Beijing, China",
  ISBN =         "0-7803-8515-2",
  keywords =     "genetic algorithms, Genetic programming, Evolutionary
                 Computation for Planning and Scheduling, Heuristic
                 Methods for Multi-Component Optimisation Problems",
  DOI =          "doi:10.1109/CEC.2014.6900347",
  abstract =     "Order acceptance and scheduling (OAS) is a hard
                 optimisation problem in which both acceptance decisions
                 and scheduling decisions must be considered
                 simultaneously. Designing effective solution methods or
                 heuristics for OAS is not a trivial task, especially to
                 deal with different problem configurations and sizes.
                 This paper proposes a new heuristic framework called
                 forward construction heuristic (FCH) for OAS and
                 develops a new sequential genetic programming (SGPOAS)
                 method for automatic design of FCHs. The key idea of
                 the new GP method is to learn priority rules directly
                 from optimal scheduling decisions at different decision
                 moments and evolve a set of rules for FCHs instead of a
                 single rule as shown in previous studies. The results
                 show that evolved FCHs are significantly better than
                 evolved single priority rules. The evolved FCHs are
                 also competitive with the existing meta-heuristics in
                 the literature and very effective for large problem
                 instances.",
  notes =        "WCCI2014",
}

Genetic Programming entries for Su Nguyen Mengjie Zhang Mark Johnston

Citations