Abstract
In retail industry, one of the most important decisions of shelf space management is the shelf location decision for products and product categories to be displayed in-store. The shelf location that products are displayed has a significant impact on product sales. At the same time, displaying complementary products close to each other increases the possibility of cross-selling of products. In this study, firstly, for a bookstore retailer, a mathematical model is developed based on association rule mining for store layout problem which includes the determination of the position of products and product categories which are displayed in-store shelves. Then, because of the NP-hard nature of the developed model, an original heuristic approach is developed based on genetic algorithms for solving large-scale real-life problems. In order to compare the performance of the genetic algorithm based heuristic with other methods, another heuristic approach based on tabu search and a simple heuristic that is commonly used by retailers are proposed. Finally, the effectiveness and applicability of the developed approaches are illustrated with numerical examples and a case study with data taken from a bookstore.
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Ozcan, T., Esnaf, S. A Discrete Constrained Optimization Using Genetic Algorithms for A Bookstore Layout. Int J Comput Intell Syst 6, 261–278 (2013). https://doi.org/10.1080/18756891.2013.768447
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DOI: https://doi.org/10.1080/18756891.2013.768447