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

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...

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Main Authors: Bai, Ruibin, Van Woensel, Tom, Kendall, Graham, Burke, Edmund K.
Format: Article
Published: Springer Verlag 2012
Subjects:
Online Access:https://eprints.nottingham.ac.uk/47518/
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author Bai, Ruibin
Van Woensel, Tom
Kendall, Graham
Burke, Edmund K.
author_facet Bai, Ruibin
Van Woensel, Tom
Kendall, Graham
Burke, Edmund K.
author_sort Bai, Ruibin
building Nottingham Research Data Repository
collection Online Access
description 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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institution University of Nottingham Malaysia Campus
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publishDate 2012
publisher Springer Verlag
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spelling nottingham-475182020-05-04T16:33:56Z https://eprints.nottingham.ac.uk/47518/ A new model and a hyper-heuristic approach for two-dimensional shelf space allocation Bai, Ruibin Van Woensel, Tom Kendall, Graham Burke, Edmund K. 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. Springer Verlag 2012-09-29 Article PeerReviewed Bai, Ruibin, Van Woensel, Tom, Kendall, Graham and Burke, Edmund K. (2012) A new model and a hyper-heuristic approach for two-dimensional shelf space allocation. 4OR: A Quarterly Journal of Operations Research, 11 (1). pp. 31-55. ISSN 1614-2411 Shelf space allocation; Two-dimensional; Retail; Multi-neighborhood search; Hyper-heuristics https://doi.org/10.1007/s10288-012-0211-2 doi:10.1007/s10288-012-0211-2 doi:10.1007/s10288-012-0211-2
spellingShingle Shelf space allocation; Two-dimensional; Retail; Multi-neighborhood search; Hyper-heuristics
Bai, Ruibin
Van Woensel, Tom
Kendall, Graham
Burke, Edmund K.
A new model and a hyper-heuristic approach for two-dimensional shelf space allocation
title A new model and a hyper-heuristic approach for two-dimensional shelf space allocation
title_full A new model and a hyper-heuristic approach for two-dimensional shelf space allocation
title_fullStr A new model and a hyper-heuristic approach for two-dimensional shelf space allocation
title_full_unstemmed A new model and a hyper-heuristic approach for two-dimensional shelf space allocation
title_short A new model and a hyper-heuristic approach for two-dimensional shelf space allocation
title_sort new model and a hyper-heuristic approach for two-dimensional shelf space allocation
topic Shelf space allocation; Two-dimensional; Retail; Multi-neighborhood search; Hyper-heuristics
url https://eprints.nottingham.ac.uk/47518/
https://eprints.nottingham.ac.uk/47518/
https://eprints.nottingham.ac.uk/47518/