Space allocation in the retail industry: A decision support system integrating evolutionary algorithms and regression models

• Type: Article in International Conference Proceedings Book
• Date: 2013

Authors

Pinto, F (Author) Other; Soares, C (Author) FEUP

Conference proceedings International

Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Pages: 531-546

European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2013

Prague, 23 September 2013 through 27 September 2013

Indexing

ISI Web of Knowledge

Scopus - 6 Citations

Scientific classification

CORDIS: Physical sciences > Computer science > Cybernetics > Artificial intelligence

FOS: Natural sciences > Computer and information sciences

Other information

Authenticus ID: P-008-FP7

DOI: 10.1007/978-3-642-40994-3_34

Abstract (EN): One of the hardest resources to manage in retail is space. Retailers need to assign limited store space to a growing number of product categories such that sales and other performance metrics are maximized. Although this seems to be an ideal task for a data mining approach, there is one important barrier: the representativeness of the available data. In fact, changes to the layout of retail stores are infrequent. This means that very few values of the space variable are represented in the data, which makes it hard to generalize. In this paper, we describe a Decision Support System to assist retailers in this task. The system uses an Evolutionary Algorithm to optimize space allocation based on the estimated impact on sales caused by changes in the space assigned to product categories. We assess the quality of the system on a real case study, using different regression algorithms to generate the estimates. The system obtained very good results when compared with the recommendations made by the business experts. We also investigated the effect of the representativeness of the sample on the accuracy of the regression models. We selected a few product categories based on a heuristic assessment of their representativeness. The results indicate that the best regression models were obtained on products for which the sample was not the best. The reason for this unexpected results remains to be explained. © 2013 Springer-Verlag.

Language: English

Type (Professor's evaluation): Scientific