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A genetic algorithm for the retail shelf space allocation problem with virtual segments

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  • Kateryna Czerniachowska

    (Wroclaw University of Economics and Business)

Abstract

Shelf space allocation is the problem of methodologically allocating products on shelves in retail stores to maximize profit, improve clients’ satisfaction, and improve stock management. Scarce shelf space is the most important and challenging resource to manage for small retailers. Most literature analyzes general models which are appropriate for many retail stores. Still, they can only be used as part of the whole process as they do not reflect complicated category management rules. In this paper, a practical shelf space allocation model is proposed, which combines retailers’ visual merchandising practices, categorized into five groups of constraint types with the aim of maximizing profit. A method is proposed to find an optimal solution for pallet shelves. The solution for other shelves was developed using a genetic algorithm, which integrates three practical techniques of solution improvement. The efficiency of the proposed approach was evaluated using CPLEX solver. The results of computational experiments show that this approach allows for perfect results for small and large product numbers in a sufficient running time.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:opsear:v:59:y:2022:i:1:d:10.1007_s12597-021-00551-3
    DOI: 10.1007/s12597-021-00551-3
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    References listed on IDEAS

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