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

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{Xie:2014:CECb,

• 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 Computation",
• 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 applications",
• 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 process.",
• notes = "WCCI2014",

}

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

Citations