Automated Design of Production Scheduling Heuristics: A Review

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

  author =       "Juergen Branke and Su Nguyen and 
                 Christoph Pickardt and Mengjie Zhang",
  title =        "Automated Design of Production Scheduling Heuristics:
                 A Review",
  journal =      "IEEE Transactions on Evolutionary Computation",
  year =         "2016",
  volume =       "20",
  number =       "1",
  pages =        "110--124",
  month =        feb,
  keywords =     "genetic algorithms, genetic programming",
  ISSN =         "1089-778X",
  DOI =          "doi:10.1109/TEVC.2015.2429314",
  size =         "15 pages",
  abstract =     "Hyper-heuristics have recently emerged as a powerful
                 approach to automate the design of heuristics for a
                 number of different problems. Production scheduling is
                 a particularly popular application area for which a
                 number of different hyperheuristics have been developed
                 and shown to be effective, efficient, easy to
                 implement, and reusable in different shop conditions.
                 In particular, they seem a promising way to tackle
                 highly dynamic and stochastic scheduling problems, an
                 aspect that is specifically emphasised in this survey.
                 Despite their success and the substantial number of
                 papers in this area, there is currently no systematic
                 discussion of the design choices and critical issues
                 involved in the process of developing such approaches.
                 This review strives to fill this gap by summarising the
                 state of the art, suggesting a taxonomy, and providing
                 the interested researchers and practitioners with
                 guidelines for the design of hyper-heuristics in
                 production scheduling. This paper also identifies
                 challenges and open questions and highlights various
                 directions for future work.",
  notes =        "This paper is a review mainly on GP methods for

                 Also known as \cite{7101236}",

Genetic Programming entries for Jurgen Branke Su Nguyen Christoph Pickardt Mengjie Zhang