Proceedings of the 3rd International Conference on Bigdata Blockchain and Economy Management, ICBBEM 2024, March 29–31, 2024, Wuhan, China

Research Article

Research on Optimal Replenishment and Pricing Decision of Vegetable Commodities

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  • @INPROCEEDINGS{10.4108/eai.29-3-2024.2347451,
        author={Fangyin  Lu and Yurui  Wu and Yining  Tian and Yaoyao  He and Wei  Huo},
        title={Research on Optimal Replenishment and Pricing Decision of Vegetable Commodities},
        proceedings={Proceedings of the 3rd International Conference on Bigdata Blockchain and Economy Management, ICBBEM 2024, March 29--31, 2024, Wuhan, China},
        publisher={EAI},
        proceedings_a={ICBBEM},
        year={2024},
        month={6},
        keywords={replenishment and pricing strategies xgboost timing prediction genetic algorithms},
        doi={10.4108/eai.29-3-2024.2347451}
    }
    
  • Fangyin Lu
    Yurui Wu
    Yining Tian
    Yaoyao He
    Wei Huo
    Year: 2024
    Research on Optimal Replenishment and Pricing Decision of Vegetable Commodities
    ICBBEM
    EAI
    DOI: 10.4108/eai.29-3-2024.2347451
Fangyin Lu1, Yurui Wu1, Yining Tian1, Yaoyao He1, Wei Huo1,*
  • 1: Northeastern University at Qinhuangdao
*Contact email: 202113675@stu.neu.edu.cn

Abstract

In the fresh food superstore, the vegetable category has a shorter freshness period and needs to make the replenishment and pricing strategy of the day according to the historical sales and demand in order to get the maximum revenue. In this paper, for the decision-making problem of maximizing the revenue of superstores, we process the data, analyze The relationship between total sales volume of vegetable categories and cost-plus pricing, construct a superstore revenue maximization model based on the unit price and sales volume, set the constraints and parameters, and give the optimal replenishment and pricing decisions.