Please use this identifier to cite or link to this item:
Title: Combining regression models and metaheuristics to optimize space allocation in the retail industry
Authors: Fábio Hernâni Pinto
Carlos Manuel Soares
Pavel Brazdil
Issue Date: 2015
Abstract: Data Mining (DM) researchers often focus on the development and testing of models for a single decision (e.g., direct mailing, churn detection, etc.). In practice, however, multiple decisions have often to be made simultaneously which are not independent and the best global solution is often not the combination of the best individual solutions. This problem can be addressed by searching for the overall best solution by using optimization methods based on the predictions made by the DM models. We describe one case study were this approach was used to optimize the layout of a retail store in order to maximize predicted sales. A metaheuristic is used to search different hypothesis of space allocations for multiple product categories, guided by the predictions made by regression models that estimate the sales for each category based on the assigned space. We test three metaheuristics and three regression algorithms on this task. Results show that the Particle Swam Optimization method guided by the models obtained with Random Forests and Support Vector Machines models obtain good results. We also provide insights about the relationship between the correctness of the regression models and the metaheuristics performance.
metadata.dc.type: article
Appears in Collections:CESE - Indexed Articles in Journals
LIAAD - Indexed Articles in Journals

Files in This Item:
File Description SizeFormat 
P-00G-SBX.pdf1.75 MBAdobe PDFView/Open

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.