Learning Reusable Initial Solutions for Multi-Objective Order Acceptance and Scheduling Problems with Genetic Programming

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

@InProceedings{nguyen:2013:EuroGP,
  author =       "Su Nguyen and Mengjie Zhang and Mark Johnston and 
                 Kay Chen Tan",
  title =        "Learning Reusable Initial Solutions for
                 Multi-Objective Order Acceptance and Scheduling
                 Problems with Genetic Programming",
  booktitle =    "Proceedings of the 16th European Conference on Genetic
                 Programming, EuroGP 2013",
  year =         "2013",
  month =        "3-5 " # apr,
  editor =       "Krzysztof Krawiec and Alberto Moraglio and Ting Hu and 
                 A. Sima Uyar and Bin Hu",
  series =       "LNCS",
  volume =       "7831",
  publisher =    "Springer Verlag",
  address =      "Vienna, Austria",
  pages =        "157--168",
  organisation = "EvoStar",
  keywords =     "genetic algorithms, genetic programming, scheduling,
                 multiple objective",
  isbn13 =       "978-3-642-37206-3",
  DOI =          "doi:10.1007/978-3-642-37207-0_14",
  abstract =     "Order acceptance and scheduling (OAS) is an important
                 issue in make-to-order production systems that decides
                 the set of orders to accept and the sequence in which
                 these accepted orders are processed to increase total
                 revenue and improve customer satisfaction. This paper
                 aims to explore the Pareto fronts of trade-off
                 solutions for a multi-objective OAS problem. Due to its
                 complexity, solving this problem is challenging. A
                 two-stage learning/optimising (2SLO) system is proposed
                 in this paper to solve the problem. The novelty of this
                 system is the use of genetic programming to evolve a
                 set of scheduling rules that can be reused to
                 initialise populations of an evolutionary
                 multi-objective optimisation (EMO) method. The
                 computational results show that 2SLO is more effective
                 than the pure EMO method. Regarding maximising the
                 total revenue, 2SLO is also competitive as compared to
                 other optimisation methods in the literature.",
  notes =        "Part of \cite{Krawiec:2013:GP} EuroGP'2013 held in
                 conjunction with EvoCOP2013, EvoBIO2013, EvoMusArt2013
                 and EvoApplications2013",
}

Genetic Programming entries for Su Nguyen Mengjie Zhang Mark Johnston Kay Chen Tan

Citations