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.

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.

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