A new model and a hyper-heuristic approach for two-dimensional shelf space allocation

Ruibin Bai, Tom van Woensel, Graham Kendall, Edmund K. Burke

Research output: Journal PublicationArticlepeer-review

30 Citations (Scopus)
3 Downloads (Pure)

Abstract

In this paper, we propose a two-dimensional shelf space allocation model. The second dimension stems from the height of the shelf. This results in an integer nonlinear programming model with a complex form of objective function. We propose a multiple neighborhood approach which is a hybridization of a simulated annealing algorithm with a hyper-heuristic learning mechanism. Experiments based on empirical data from both real-world and artificial instances show that the shelf space utilization and the resulting sales can be greatly improved when compared with a gradient method. Sensitivity analysis on the input parameters and the shelf space show the benefits of the proposed algorithm both in sales and in robustness.

Original languageEnglish
Pages (from-to)31-55
Number of pages25
Journal4OR
Volume11
Issue number1
DOIs
Publication statusPublished - Mar 2013

Keywords

  • Hyper-heuristics
  • Multi-neighborhood search
  • Retail
  • Shelf space allocation
  • Two-dimensional

ASJC Scopus subject areas

  • Management Information Systems
  • Theoretical Computer Science
  • Management Science and Operations Research
  • Computational Theory and Mathematics

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