A restricted neighbourhood Tabu Search for Storage Location Assignment Problem

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

  author =       "Jing Xie and Yi Mei and Andreas T. Ernst and 
                 Xiaodong Li and Andy Song",
  booktitle =    "2015 IEEE Congress on Evolutionary Computation (CEC)",
  title =        "A restricted neighbourhood Tabu Search for Storage
                 Location Assignment Problem",
  year =         "2015",
  pages =        "2805--2812",
  abstract =     "The Storage Location Assignment Problem (SLAP) is a
                 significant optimisation problem in warehouse
                 management. Given a number of products, each with a set
                 of items with different popularities (probabilities of
                 being ordered), SLAP is to find the best locations for
                 the items of the products in the warehouse to minimise
                 the warehouse operational cost. Specifically, the
                 operational cost is the expected cost of picking the
                 orders. Grouping constraints are included to take the
                 practical considerations into account in the problem.
                 That is, the items belonging to the same product are
                 more desirable to be placed together. In this paper,
                 the SLAP with Grouping Constraints (SLAP-GC) is
                 investigated, and an efficient Restricted Neighbourhood
                 Tabu Search (RNTS) algorithm is proposed to solving it.
                 RNTS adopts the problem-specific search operators to
                 maintain solution feasibility, and the tabu list to
                 prevent searching back and forth. RNTS was empirically
                 compared with the mathematical programming method and a
                 previously designed Genetic Programming method, which
                 is demonstrated to be the state-of-the-art algorithm
                 for SLAP-GC. The experimental results on the real-world
                 data show that RNTS outperforms the state-of-the-art
                 algorithms for SLAP-GC in terms of solution quality and
                 speed. It managed to achieve optimal solutions for most
                 of the small-scale instances much faster and
                 outperformed the Genetic Programming method in terms of
                 both solution quality and running time on all the test
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/CEC.2015.7257237",
  ISSN =         "1089-778X",
  month =        may,
  notes =        "Sch. of Comput. Sci. & IT, RMIT Univ., Melbourne, VIC,

                 Also known as \cite{7257237}",

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