Academic Journal of Mathematical Sciences, 2024, 5(2); doi: 10.25236/AJMS.2024.050206.
Chuanyang Zha1, Guokai Shi2, Yingying Luo3
1School of Materials Science and Engineering, Xi’an Shiyou University, Xi’an, 710065, China
2Mechanical Engineering College, Xi’an Shiyou University, Xi’an, 710065, China
3School of Electronic Engineering, Xi’an Shiyou University, Xi’an, 710065, China
Generally the freshness period of vegetables is shorter, and its sales are greatly affected by time, in order to improve its profitability, so it is particularly important to develop a reasonable replenishment and pricing strategy. In this paper, firstly, the sales data of vegetable commodities were analyzed by descriptive statistics and normality test, and found that the distribution pattern is normal; then Pearson correlation analysis was used to find out the correlation between various categories of vegetables, and it was concluded that the correlation is stronger between cauliflower and foliage, edible mushrooms and aquatic roots and tubers. Vegetable products are the necessities of residents' life, but vegetable products have short freshness period and easy to deteriorate and other problems, so it is necessary to replenish the goods every day, and the goods that have not been bought out should be sold at a discount in a timely manner, in view of the market demand and the interests of the superstore's own needs, so the reasonable replenishment and pricing strategy is also particularly important.
K-means cluster analysis, Linear regression, LSTM time series modeling, Particle swarm algorithm
Chuanyang Zha, Guokai Shi, Yingying Luo. A mathematical model for automated pricing and replenishment decisions for vegetable items. Academic Journal of Mathematical Sciences (2024) Vol. 5, Issue 2: 32-40. https://doi.org/10.25236/AJMS.2024.050206.
[1] Cui Ligang, Li Yali, Liu Jinxing et al. Joint decision making for multi-product replenishment and pricing considering investment in preservation technology [J]. Industrial Engineering and Management, 2023, 28(03):17-26.
[2] Yang Shuai, Huang Xiangmeng, Wang Junbin. Research on joint optimization strategy of shelf allocation and pricing for fresh food [J]. Supply Chain Management, 2022, 3(08):49-59.
[3] Chen Ming, Li Junxiang, Qu Deqiang et al. Planning of urban emergency logistics facilities based on K-means clustering algorithm - an example of vegetable delivery data at a certain stage in Changchun City[J]. Logistics Science and Technology, 2023, 46(17):57-60.
[4] Liu Quanhong, Tang Fuxing. Optimization of site selection for faulty shared bicycle recycling center based on K-means clustering algorithm and center of gravity method [J]. Operations Research and Management, 2023, 32(07):85-91.
[5] Kowsar T, Zeinab T, Negin D. Ensemble models based on CNN and LSTM for dropout prediction in MOOC [J]. Expert Systems with Applications, 2024, 235.