Learning single-machine scheduling heuristics subject to machine breakdowns with genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4333

  author =       "Wen-Jun Yin and Min Liu and Cheng Wu",
  title =        "Learning single-machine scheduling heuristics subject
                 to machine breakdowns with genetic programming",
  booktitle =    "Proceedings of the 2003 Congress on Evolutionary
                 Computation CEC2003",
  editor =       "Ruhul Sarker and Robert Reynolds and 
                 Hussein Abbass and Kay Chen Tan and Bob McKay and Daryl Essam and 
                 Tom Gedeon",
  pages =        "1050--1055",
  year =         "2003",
  publisher =    "IEEE Press",
  volume =       "2",
  address =      "Canberra",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "8-12 " # dec,
  organisation = "IEEE Neural Network Council (NNC), Engineers Australia
                 (IEAust), Evolutionary Programming Society (EPS),
                 Institution of Electrical Engineers (IEE)",
  keywords =     "genetic algorithms, genetic programming, GP-evolved
                 heuristics, bi-tree structured representation, idle
                 time inserting programs, machine breakdowns, predictive
                 scheduling heuristics, single-machine scheduling,
                 heuristic programming, job shop scheduling, single
                 machine scheduling, stochastic programming, tree
  ISBN =         "0-7803-7804-0",
  DOI =          "doi:10.1109/CEC.2003.1299784",
  abstract =     "Genetic Programming (GP) has been rarely applied to
                 scheduling problems. In this paper the use of GP to
                 learn single-machine predictive scheduling (PS)
                 heuristics with stochastic breakdowns is investigated,
                 where both tardiness and stability objectives in face
                 of machine failures are considered. The proposed
                 bi-tree structured representation scheme makes it
                 possible to search sequencing and idle time inserting
                 programs together. Empirical results in different
                 uncertain environments show that GP can evolve high
                 quality PS heuristics effectively. The roles of
                 inserted idle time are then analysed with respect to
                 various weighting objectives. Finally some guides are
                 supplied for PS design based on GP-evolved
  notes =        "Also known as \cite{1299784}.

                 CEC 2003 - A joint meeting of the IEEE, the IEAust, the
                 EPS, and the IEE.",

Genetic Programming entries for Wen-Jun Yin Min Liu Cheng Wu