A Genetic Programming-Based Hyper-heuristic Approach for Storage Location Assignment Problem

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  title =        "A Genetic Programming-Based Hyper-heuristic Approach
                 for Storage Location Assignment Problem",
  author =       "Jing Xie and Yi Mei and Andreas Ernst and 
                 Xiaodong Li and Andy Song",
  pages =        "3000--3007",
  booktitle =    "Proceedings of the 2014 IEEE Congress on Evolutionary
  year =         "2014",
  month =        "6-11 " # jul,
  editor =       "Carlos A. {Coello Coello}",
  address =      "Beijing, China",
  ISBN =         "0-7803-8515-2",
  keywords =     "genetic algorithms, genetic programming, Real-world
  URL =          "http://goanna.cs.rmit.edu.au/~e04499/Papers/CEC14-JingMeiErnstLiSong.pdf",
  DOI =          "doi:10.1109/CEC.2014.6900604",
  size =         "8 pages",
  abstract =     "This study proposes a method for solving real-world
                 warehouse Storage Location Assignment Problem (SLAP)
                 under grouping constraints by Genetic Programming (GP).
                 Integer Linear Programming (ILP) formulation is used to
                 define the problem. By the proposed GP method, a subset
                 of the items is repeatedly selected and placed into the
                 available current best location of the shelves in the
                 warehouse, until all the items have been assigned with
                 locations. A heuristic matching function is evolved by
                 GP to guide the selection of the subsets of items. Our
                 comparison between the proposed GP approach and the
                 traditional ILP approach shows that GP can obtain
                 near-optimal solutions on the training data within a
                 short period of time. Moreover, the evolved heuristics
                 can achieve good optimisation results on unseen
                 scenarios, comparable to that on the scenario used for
                 training. This shows that the evolved heuristics have
                 good reusability and can be directly applied for
                 slightly different scenarios without any new search
  notes =        "WCCI2014",

Genetic Programming entries for Jing Xie Yi Mei Andreas Ernst Xiaodong Li Andy Song