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Profit maximizing through 3D shelf space allocation of 2D display orientation items with variable heights of the shelves

Author

Listed:
  • Masoud Rabbani

    (University of Tehran)

  • Navid Salmanzadeh-Meydani

    (University of Tehran)

  • Amir Farshbaf-Geranmayeh

    (University of Tehran
    HEC Montréal)

  • Vahed Fadakar-Gabalou

    (University of Tehran)

Abstract

In this paper, a shelf retail decision making model is proposed and examined the effect of different factors such as vertical and horizontal location, product cross elasticity, the number of items in eye-level, etc. The success of a retailer depends on his/her ability to adapt environmental changes through continuous decision making about how much and which product, should be placed from which horizontal level of which shelf, and with what display orientation. In this paper, shelves are considered 3-dimensionally, and in addition to the length and width of the shelves, the depth of the shelves is also effective in decision making. The height of the shelves has been considered as a variable, and its optimal value would be obtained by solving the proposed mathematical model. Considering the third dimension of shelf makes it possible to use the back space of shelves for the purpose of holding the inventory. In this paper, two display orientations for the items are considered and each orientation can have a different effect on selling the product. Items can be stacked on the shelves, but the note is that it is not possible to stack some of the item. By considering the third dimension, the total inventory and inventory of each product in the store can be obtained. For solving the model, genetic algorithm (GA) is proposed and the Taguchi method is applied for tuning parameter of the GA.

Suggested Citation

  • Masoud Rabbani & Navid Salmanzadeh-Meydani & Amir Farshbaf-Geranmayeh & Vahed Fadakar-Gabalou, 2018. "Profit maximizing through 3D shelf space allocation of 2D display orientation items with variable heights of the shelves," OPSEARCH, Springer;Operational Research Society of India, vol. 55(2), pages 337-360, June.
    • Handle: RePEc:spr:opsear:v:55:y:2018:i:2:d:10.1007_s12597-018-0335-z
    DOI: 10.1007/s12597-018-0335-z
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    References listed on IDEAS

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    1. Tsao, Yu-Chung & Lu, Jye-Chyi & An, Na & Al-Khayyal, Faiz & Lu, Richard W. & Han, Guanghua, 2014. "Retailer shelf-space management with trade allowance: A Stackelberg game between retailer and manufacturers," International Journal of Production Economics, Elsevier, vol. 148(C), pages 133-144.
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    Cited by:

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    2. 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.
    3. Monika Matušovičová & Martin Kuchta & Monika Stanková, 2022. "Improvement in Customer Experience Through the Creation of Virtual Brand Communities," Tržište/Market, Faculty of Economics and Business, University of Zagreb, vol. 34(1), pages 79-92.
    4. Gecili, Hakan & Parikh, Pratik J., 2022. "Joint shelf design and shelf space allocation problem for retailers," Omega, Elsevier, vol. 111(C).
    5. 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.
    6. Kim, Gwang & Moon, Ilkyeong, 2021. "Integrated planning for product selection, shelf-space allocation, and replenishment decision with elasticity and positioning effects," Journal of Retailing and Consumer Services, Elsevier, vol. 58(C).
    7. 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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