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Allocating products on shelves under merchandising rules: Multi-level product families with display directions

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  • Bianchi-Aguiar, Teresa
  • Silva, Elsa
  • Guimarães, Luis
  • Carravilla, Maria Antónia
  • Oliveira, José F.

Abstract

Retailers’ individual products are categorized as part of product families. Merchandising rules specify how the products should be arranged on the shelves using product families, creating more structured displays capable of increasing the viewers’ attention. This paper presents a novel mixed integer programming formulation for the Shelf Space Allocation Problem considering two innovative features emerging from merchandising rules: hierarchical product families and display directions. The formulation uses single commodity flow constraints to model product sequencing and explores the product families’ hierarchy to reduce the combinatorial nature of the problem. Based on the formulation, a mathematical programming-based heuristic was also developed that uses product families to decompose the problem into a sequence of sub-problems. To improve performance, its original design was adapted following two directions: recovery from infeasible solutions and reduction of solution times. A new set of real case benchmark instances is also provided, which was used to assess the formulation and the matheuristic. This approach will allow retailers to efficiently create planograms capable of following merchandising rules and optimizing shelf space revenue.

Suggested Citation

  • Bianchi-Aguiar, Teresa & Silva, Elsa & Guimarães, Luis & Carravilla, Maria Antónia & Oliveira, José F., 2018. "Allocating products on shelves under merchandising rules: Multi-level product families with display directions," Omega, Elsevier, vol. 76(C), pages 47-62.
  • Handle: RePEc:eee:jomega:v:76:y:2018:i:c:p:47-62
    DOI: 10.1016/j.omega.2017.04.002
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    References listed on IDEAS

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    Cited by:

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    3. Kateryna Czerniachowska, 2022. "A genetic algorithm for the retail shelf space allocation problem with virtual segments," OPSEARCH, Springer;Operational Research Society of India, vol. 59(1), pages 364-412, March.
    4. Bianchi-Aguiar, Teresa & Hübner, Alexander & Carravilla, Maria Antónia & Oliveira, José Fernando, 2021. "Retail shelf space planning problems: A comprehensive review and classification framework," European Journal of Operational Research, Elsevier, vol. 289(1), pages 1-16.
    5. Kateryna Czerniachowska & Marcin Hernes, 2020. "A Genetic Algorithm for the Shelf-Space Allocation Problem with Vertical Position Effects," Mathematics, MDPI, vol. 8(11), pages 1-20, October.
    6. Kateryna Czerniachowska & Krzysztof Michalak & Marcin Hernes, 2023. "Heuristics for the shelf space allocation problem," OPSEARCH, Springer;Operational Research Society of India, vol. 60(2), pages 835-869, June.
    7. Schäfer, Fabian & Hense, Jonas & Hübner, Alexander, 2023. "An analytical assessment of demand effects in omni-channel assortment planning," Omega, Elsevier, vol. 115(C).
    8. Caglar Gencosman, Burcu & Begen, Mehmet A., 2022. "Exact optimization and decomposition approaches for shelf space allocation," European Journal of Operational Research, Elsevier, vol. 299(2), pages 432-447.

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