Name
Title | ||
|---|---|---|
A new model and a hyper-heuristic approach for two-dimensional shelf space allocation. |
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. © 2012 Springer-Verlag. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/s10288-012-0211-2 | 4OR |
Keywords | Field | DocType |
hyper-heuristics,multi-neighborhood search,retail,shelf space allocation,two-dimensional,two dimensional | Gradient method,Simulated annealing,Mathematical optimization,Hyper-heuristic,Robustness (computer science),Nonlinear mixed integer programming,Space allocation,Mathematics | Journal |
Volume | Issue | ISSN |
11 | 1 | 16142411 |
Citations | PageRank | References |
7 | 0.53 | 15 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ruibin Bai | 1 | 168 | 13.65 |
| eu | 2 | 549 | 39.51 |
| Graham Kendall | 3 | 607 | 35.33 |
| Edmund K. Burke | 4 | 5593 | 363.80 |