A PSO-based hyper-heuristic for evolving dispatching rules in job shop scheduling

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

@InProceedings{nguyen:2017:CECa,
  author =       "Su Nguyen and Mengjie Zhang",
  booktitle =    "2017 IEEE Congress on Evolutionary Computation (CEC)",
  title =        "A PSO-based hyper-heuristic for evolving dispatching
                 rules in job shop scheduling",
  year =         "2017",
  editor =       "Jose A. Lozano",
  pages =        "882--889",
  address =      "Donostia, San Sebastian, Spain",
  publisher =    "IEEE",
  isbn13 =       "978-1-5090-4601-0",
  abstract =     "Automated heuristic design for job shop scheduling has
                 been an interesting and challenging research topic in
                 the last decade. Various machine learning and
                 optimising techniques, usually referred to as
                 hyper-heuristics, have been applied to facilitate the
                 design task. Two main approaches are either to use a
                 general structure for dispatching rules and optimise
                 its parameters or to simultaneously search for suitable
                 structures and their parameters. Each approach has its
                 own advantages and disadvantages. In this paper, we
                 focus on the first approach and develop new
                 representations that are flexible enough to represent
                 diverse rules and powerful enough to cope with complex
                 shop conditions. Particle swarm optimisation is used in
                 the proposed hyper-heuristic to find optimal rules
                 based on the representations. The results suggest that
                 the new representations are effective for different
                 shop conditions and obtained rules are very competitive
                 as compared to those evolved by genetic programming.
                 Analyses also show that the proposed hyper-heuristic is
                 significantly faster than genetic programming based
                 hyper-heuristic.",
  keywords =     "genetic algorithms, genetic programming, dispatching,
                 job shop scheduling, particle swarm optimisation,
                 PSO-based hyperheuristic, automated heuristic design,
                 dispatching rules, machine learning, optimising
                 techniques, Neural networks, Optimization methods,
                 Particle swarm optimization, Processor scheduling,
                 evolutionary design, hyper-heuristic, scheduling",
  isbn13 =       "978-1-5090-4601-0",
  DOI =          "doi:10.1109/CEC.2017.7969402",
  month =        "5-8 " # jun,
  notes =        "IEEE Catalog Number: CFP17ICE-ART Also known as
                 \cite{7969402}",
}

Genetic Programming entries for Su Nguyen Mengjie Zhang

Citations