IDEAS home Printed from https://ideas.repec.org/a/spr/opsear/v55y2018i2d10.1007_s12597-018-0335-z.html
   My bibliography  Save this article

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

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12597-018-0335-z
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    2. Hansen, Jared M. & Raut, Sumit & Swami, Sanjeev, 2010. "Retail Shelf Allocation: A Comparative Analysis of Heuristic and Meta-Heuristic Approaches," Journal of Retailing, Elsevier, vol. 86(1), pages 94-105.
    3. Murray, Chase C. & Talukdar, Debabrata & Gosavi, Abhijit, 2010. "Joint Optimization of Product Price, Display Orientation and Shelf-Space Allocation in Retail Category Management," Journal of Retailing, Elsevier, vol. 86(2), pages 125-136.
    4. Eisend, Martin, 2014. "Shelf space elasticity: A meta-analysis," Journal of Retailing, Elsevier, vol. 90(2), pages 168-181.
    5. Leng, Mingming & Parlar, Mahmut & Zhang, Dengfeng, 2014. "Cooperative game analysis of retail space-exchange problems," European Journal of Operational Research, Elsevier, vol. 232(2), pages 393-404.
    6. Yang, Ming-Hsien, 2001. "An efficient algorithm to allocate shelf space," European Journal of Operational Research, Elsevier, vol. 131(1), pages 107-118, May.
    7. Hübner, Alexander & Schaal, Kai, 2017. "A shelf-space optimization model when demand is stochastic and space-elastic," Omega, Elsevier, vol. 68(C), pages 139-154.
    8. Hwang, Hark & Choi, Bum & Lee, Min-Jin, 2005. "A model for shelf space allocation and inventory control considering location and inventory level effects on demand," International Journal of Production Economics, Elsevier, vol. 97(2), pages 185-195, August.
    9. Piramuthu, Selwyn & Zhou, Wei, 2013. "RFID and perishable inventory management with shelf-space and freshness dependent demand," International Journal of Production Economics, Elsevier, vol. 144(2), pages 635-640.
    10. Sheng-Chih Chen & Jie Min & Jinn-Tsair Teng & Fuan Li, 2016. "Inventory and shelf-space optimization for fresh produce with expiration date under freshness-and-stock-dependent demand rate," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 67(6), pages 884-896, June.
    11. Hübner, Alexander H. & Kuhn, Heinrich, 2012. "Retail category management: State-of-the-art review of quantitative research and software applications in assortment and shelf space management," Omega, Elsevier, vol. 40(2), pages 199-209, April.
    12. van Zelst, Susan & van Donselaar, Karel & van Woensel, Tom & Broekmeulen, Rob & Fransoo, Jan, 2009. "Logistics drivers for shelf stacking in grocery retail stores: Potential for efficiency improvement," International Journal of Production Economics, Elsevier, vol. 121(2), pages 620-632, October.
    13. H. Neil Geismar & Milind Dawande & B. P. S. Murthi & Chelliah Sriskandarajah, 2015. "Maximizing Revenue Through Two-Dimensional Shelf-Space Allocation," Production and Operations Management, Production and Operations Management Society, vol. 24(7), pages 1148-1163, July.
    14. Andrew Lim & Brian Rodrigues & Xingwen Zhang, 2004. "Metaheuristics with Local Search Techniques for Retail Shelf-Space Optimization," Management Science, INFORMS, vol. 50(1), pages 117-131, January.
    15. Hübner, Alexander & Schaal, Kai, 2017. "An integrated assortment and shelf-space optimization model with demand substitution and space-elasticity effects," European Journal of Operational Research, Elsevier, vol. 261(1), pages 302-316.
    Full references (including those not matched with items on IDEAS)

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:opsear:v:55:y:2018:i:2:d:10.1007_s12597-018-0335-z. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Sonal Shukla) or (Springer Nature Abstracting and Indexing). General contact details of provider: http://www.springer.com .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.