Academic Journal of Computing & Information Science, 2024, 7(7); doi: 10.25236/AJCIS.2024.070709.

## Construction and analysis of optimal decision-making model for vegetable sales based on entropy weight-TOPSIS algorithm and gray prediction

Author(s)

Yuanbo Ding

Corresponding Author:
Yuanbo Ding
Affiliation(s)

Department of Computer and Information, Hefei University of Technology, Hefei, 230601, China

### Abstract

Vegetable commodities have a short freshness period and need to be replenished every day, to develop the optimal pricing and replenishment strategy, this paper develops the optimal pricing and replenishment strategy by comprehensively applying the entropy-weight-TOPSIS algorithm, gray prediction, and nonlinear programming. Through the entropy weight-TOPSIS algorithm, the single product was evaluated by multiple indicators, and the top 27 vegetable products were selected for subsequent research. The average selling price, cost, and total sales volume of each type of vegetables were obtained through gray prediction. The penalty function model of sales volume and average selling price was established based on regression analysis, and the fitting effect was good. Finally, the optimal decision-making model was established through nonlinear programming, with maximizing profit as the objective function, and combined with the fluctuation constraints of the selling price, the optimal solutions of the maximum profit value of 520.897 and the selling unit price were successfully solved. Meanwhile, the replenishment decision under the optimal selling unit price is proposed by combining the penalty function model and the attrition rate.

### Keywords

Nonlinear Programming, Gray Prediction, Entropy Weight-TOPSIS

### Cite This Paper

Yuanbo Ding. Construction and analysis of optimal decision-making model for vegetable sales based on entropy weight-TOPSIS algorithm and gray prediction. Academic Journal of Computing & Information Science (2024), Vol. 7, Issue 7: 65-72. https://doi.org/10.25236/AJCIS.2024.070709.

### References

[1] LI Xiaolu, ZHOU Shuguang. Research on the Development Problems of Fresh Super Retail Industry in China [J]. Commercial Economy Research, 2021(23):35-37

[2] Yanping L , Li L , Teng L .Multi-objective Aerodynamic and Stealthy Performance Optimization Based on Multi-attribute Decision Making[J].Journal of Mechanical Engineering, 2012, 48(13):132.DOI:10.3901/JME.2012.13.132.

[3] Lu Yajie. Research on dynamic pricing problem of high quality fresh vegetables in supermarkets in China [D]. Beijing Jiaotong University, 2010.

[4] Fan Jing. Research on Inventory and Pricing Strategies of Omni-Channel Supply Chain for Fresh Agricultural Products [D]. South China University of Technology, 2021.

[5] Tseng, M. M. Research on Dynamic Pricing Strategies of Fresh Community Supermarket Based on Time Situation A[D]. Southwest University of Finance and Economics, 2021.

[6] Chen Jun,Kang Sha. Joint decision making for pricing and inventory replenishment of agricultural products for dual-channel sales[J]. Industrial Engineering, 2023, 26(03):39-46.

[7] Mao Lisha. Research on pricing strategy and production and marketing model of vegetable wholesale market under the perspective of supply chain[D]. Central South Forestry University of Science and Technology, 2022.

[8] REN Xi-Qing, ZHANG Zhen-Hua, ZHOU Ye-Fu. Optimization design of supply chain of fresh vegetable industry under sudden public health event[J]. Times Economy and Trade, 2024, (2)

[9] WANG Zhe, YANG Qushi, LU Hao et al. Research on revenue optimization and pricing strategy of superstore based on machine learning[J]. Computer Science and Applications, 2023, (12):2623-2628.

[10] CHI Song-Heng, ZHANG Qin-Hong. A study on joint decision making of presale inventory and pricing in fresh food e-commerce considering consumers' freshness preference[J]. Logistics Engineering and Management, 2021, Vol. 43(8):116-125.