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A new optimization model for market basket analysis with allocation considerations: A genetic algorithm solution approach

Author

Listed:
  • Heydari Majeed

    (University of Zanjan, Zanjan, Iran (Islamic Republic of))

  • Yousefli Amir

    (Imam Khomeini International University, Qazvin, Iran (Islamic Republic of))

Abstract

Nowadays market basket analysis is one of the interested research areas of the data mining that has received more attention by researchers. But, most of the related research focused on the traditional and heuristic algorithms with limited factors that are not the only influential factors of the basket market analysis. In this paper to efficient modeling and analysis of the market basket data, the optimization model is proposed with considering allocation parameter as one of the important and effectual factors of the selling rate. The genetic algorithm approach is applied to solve the formulated non-linear binary programming problem and a numerical example is used to illustrate the presented model. The provided results reveal that the obtained solutions seem to be more realistic and applicable.

Suggested Citation

  • Heydari Majeed & Yousefli Amir, 2017. "A new optimization model for market basket analysis with allocation considerations: A genetic algorithm solution approach," Management & Marketing, Sciendo, vol. 12(1), pages 1-11, March.
    • Handle: RePEc:vrs:manmar:v:12:y:2017:i:1:p:1-11:n:1
    DOI: 10.1515/mmcks-2017-0001
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    References listed on IDEAS

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    1. O. L. Mangasarian, 1965. "Linear and Nonlinear Separation of Patterns by Linear Programming," Operations Research, INFORMS, vol. 13(3), pages 444-452, June.
    2. Erjen van Nierop & Dennis Fok & Philip Hans Franses, 2008. "Interaction Between Shelf Layout and Marketing Effectiveness and Its Impact on Optimizing Shelf Arrangements," Marketing Science, INFORMS, vol. 27(6), pages 1065-1082, 11-12.
    3. Balaji Padmanabhan & Alexander Tuzhilin, 2003. "On the Use of Optimization for Data Mining: Theoretical Interactions and eCRM Opportunities," Management Science, INFORMS, vol. 49(10), pages 1327-1343, October.
    4. P. S. Bradley & Usama M. Fayyad & O. L. Mangasarian, 1999. "Mathematical Programming for Data Mining: Formulations and Challenges," INFORMS Journal on Computing, INFORMS, vol. 11(3), pages 217-238, August.
    5. Olafsson, Sigurdur & Li, Xiaonan & Wu, Shuning, 2008. "Operations research and data mining," European Journal of Operational Research, Elsevier, vol. 187(3), pages 1429-1448, June.
    6. Alina BARBU, 2013. "Eight contemporary trends in the market research industry," Management & Marketing, Economic Publishing House, vol. 8(3), Autumn.
    7. Robert Russell & Timothy Urban, 2010. "The location and allocation of products and product families on retail shelves," Annals of Operations Research, Springer, vol. 179(1), pages 131-147, September.
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