A Discrete Constrained Optimization Using Genetic Algorithms for A Bookstore Layout

Özcan T. , Esnaf S.

INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS, vol.6, no.2, pp.261-278, 2013 (Journal Indexed in SCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 6 Issue: 2
  • Publication Date: 2013
  • Doi Number: 10.1080/18756891.2013.768447
  • Page Numbers: pp.261-278


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.