A Discrete Constrained Optimization Using Genetic Algorithms for A Bookstore Layout
- https://doi.org/10.1080/18756891.2013.768447How to use a DOI?
- Store layout, Shelf location, Genetic algorithms, Tabu search, Association rule mining
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.
- © 2017, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - JOUR AU - Tuncay Ozcan AU - Sakir Esnaf PY - 2013 DA - 2013/03/01 TI - A Discrete Constrained Optimization Using Genetic Algorithms for A Bookstore Layout JO - International Journal of Computational Intelligence Systems SP - 261 EP - 278 VL - 6 IS - 2 SN - 1875-6883 UR - https://doi.org/10.1080/18756891.2013.768447 DO - https://doi.org/10.1080/18756891.2013.768447 ID - Ozcan2013 ER -