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Utilization of Genetic Algorithm in Allocating Goods to Shop Shelves Under an Application to Cup Noodles

Author

Listed:
  • Yuki Higuchi
  • Koumei Suzuki
  • Kazuhiro Takeyasu

Abstract

How to allocate goods in shop shelves makes great influence to sales amount. Searching best fit allocation of goods to shelves is a kind of combinatorial problem. This becomes a problem of integer programming and utilizing genetic algorithm may be an effective method. Reviewing past researches, there are few researches made on this. Formerly, we have presented papers concerning optimization in allocating goods to shop shelves utilizing genetic algorithm. In those papers, the problem that goods were not allowed to allocate in multiple shelves and the problem that goods were allowed to allocate in multiple shelves were pursued. In this paper, we examine the problem that does not allow goods to be allocated in multiple shelves and introduce the concept of sales profits and sales probabilities. Expansion of shelf is executed. Optimization in allocating goods to shop shelves is investigated. An application to the convenience store with POS sales data of cup noodles is executed. Utilizing genetic algorithm, optimum solution is pursued and verified by a numerical example. Comparison with other past papers was executed. Various patterns of problems must be examined hereafter.

Suggested Citation

  • Yuki Higuchi & Koumei Suzuki & Kazuhiro Takeyasu, 2019. "Utilization of Genetic Algorithm in Allocating Goods to Shop Shelves Under an Application to Cup Noodles," International Journal of Business Administration, International Journal of Business Administration, Sciedu Press, vol. 10(3), pages 104-117, May.
    • Handle: RePEc:jfr:ijba11:v:10:y:2019:i:3:p:104-117
    DOI: 10.5430/ijba.v10n3p104
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    References listed on IDEAS

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    1. Kazuhiro Takeyasu & Yuki Higuchi, 2016. "Utilization of Genetic Algorithm in Allocating Goods to Shop Shelves," Business and Management Research, Business and Management Research, Sciedu Press, vol. 5(4), pages 1-13, December.
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