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Combining regression models and metaheuristics to optimize space allocation in the retail industry.

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  • Additional Information
    • Author-Supplied Keywords:
      Data Mining
      metaheuristics
      retail
      space allocation
    • NAICS/Industry Codes:
      452999 All other miscellaneous general merchandise stores
      453998 All Other Miscellaneous Store Retailers (except Tobacco Stores)
      453999 All other miscellaneous store retailers (except beer and wine-making supplies stores)
    • 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. [ABSTRACT FROM AUTHOR]
    • Abstract:
      Copyright of Intelligent Data Analysis is the property of IOS Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
    • Author Affiliations:
      1LIAAD - INESC TEC/FEP, Universidade do Porto, Porto, Portugal
      2CESE - INESC TEC/FEUP, Universidade do Porto, Porto, Portugal
    • ISSN:
      1088-467X
    • Accession Number:
      10.3233/IDA-150775
    • Accession Number:
      110394892
  • Citations
    • ABNT:
      PINTO, F.; SOARES, C.; BRAZDIL, P. Combining regression models and metaheuristics to optimize space allocation in the retail industry. Intelligent Data Analysis, [s. l.], v. 19, p. S149–S162, 2015. DOI 10.3233/IDA-150775. Disponível em: http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=asn&AN=110394892&custid=s8280428. Acesso em: 5 jul. 2020.
    • AMA:
      Pinto F, Soares C, Brazdil P. Combining regression models and metaheuristics to optimize space allocation in the retail industry. Intelligent Data Analysis. 2015;19:S149-S162. doi:10.3233/IDA-150775.
    • AMA11:
      Pinto F, Soares C, Brazdil P. Combining regression models and metaheuristics to optimize space allocation in the retail industry. Intelligent Data Analysis. 2015;19:S149-S162. doi:10.3233/IDA-150775
    • APA:
      Pinto, F., Soares, C., & Brazdil, P. (2015). Combining regression models and metaheuristics to optimize space allocation in the retail industry. Intelligent Data Analysis, 19, S149–S162. https://doi.org/10.3233/IDA-150775
    • Chicago/Turabian: Author-Date:
      Pinto, Fábio, Carlos Soares, and Pavel Brazdil. 2015. “Combining Regression Models and Metaheuristics to Optimize Space Allocation in the Retail Industry.” Intelligent Data Analysis 19 (September): S149–62. doi:10.3233/IDA-150775.
    • Harvard:
      Pinto, F., Soares, C. and Brazdil, P. (2015) ‘Combining regression models and metaheuristics to optimize space allocation in the retail industry’, Intelligent Data Analysis, 19, pp. S149–S162. doi: 10.3233/IDA-150775.
    • Harvard: Australian:
      Pinto, F, Soares, C & Brazdil, P 2015, ‘Combining regression models and metaheuristics to optimize space allocation in the retail industry’, Intelligent Data Analysis, vol. 19, pp. S149–S162, viewed 5 July 2020, .
    • MLA:
      Pinto, Fábio, et al. “Combining Regression Models and Metaheuristics to Optimize Space Allocation in the Retail Industry.” Intelligent Data Analysis, vol. 19, Sept. 2015, pp. S149–S162. EBSCOhost, doi:10.3233/IDA-150775.
    • Chicago/Turabian: Humanities:
      Pinto, Fábio, Carlos Soares, and Pavel Brazdil. “Combining Regression Models and Metaheuristics to Optimize Space Allocation in the Retail Industry.” Intelligent Data Analysis 19 (September 2, 2015): S149–62. doi:10.3233/IDA-150775.
    • Vancouver/ICMJE:
      Pinto F, Soares C, Brazdil P. Combining regression models and metaheuristics to optimize space allocation in the retail industry. Intelligent Data Analysis [Internet]. 2015 Sep 2 [cited 2020 Jul 5];19:S149–62. Available from: http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=asn&AN=110394892&custid=s8280428