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.
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Bai, R., van Woensel, T., Kendall, G. et al. A new model and a hyper-heuristic approach for two-dimensional shelf space allocation. 4OR-Q J Oper Res 11, 31–55 (2013). https://doi.org/10.1007/s10288-012-0211-2
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DOI: https://doi.org/10.1007/s10288-012-0211-2