A Genetic Programming-Based Hyper-heuristic Approach for Storage Location Assignment Problem
Created by W.Langdon from
gp-bibliography.bib Revision:1.7970
- @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