An optimal bread delivery method and system under random distribution

Abstract

The invention relates to an optimal bread supply method and a system under random distribution. The method comprises the following steps: first, simulating a Poisson process of bread sales; 2, the probability distribution of bread sales is modified; 3, establish the probability model of bread sale; 4, establish a daily supply quantity table; the model in the invention studies the probability distribution of daily bread sales, can well explain the randomness of bread sales, and with the updating of sales data, the probability distribution of bread sales is modified in real time, so as to improve the generalization of the model. In the invention, the probability model is used to study the optimal supply quantity strategy of the next day's bread sales, which has little dependence on data anddoes not need a large amount of data to corroborate the sales law among the data. The invention focuses on the small and medium-sized bread retailers, helps them to effectively manage the sales and operation of bread products, effectively predicts the optimal supply quantity of the next day's products, and achieves the scientific decision of the bread sales strategy.

Description

technical field [0001] The invention belongs to the field of optimal supply strategies for time-sensitive commodities, and in particular relates to an optimal bread supply method and system under random distribution. Background technique [0002] Bread is a commodity with time-sensitive characteristics. It has a short shelf life and may show periodic characteristics (for example, the sales volume on holidays and weekdays is different, showing a certain period). In the market, companies that produce time-sensitive commodities will have The product supply and sales system makes scientific decisions on product development based on methods such as product market research and historical sales record analysis. However, for general small and medium-sized retailers, such as bakeries and cake shops, decisions are made on the supply of time-sensitive products based on sales experience, which makes retailers have certain difficulties in the management and sales of time-sensitive produc...

AI Technical Summary

Problems solved by technology

[0006] 3. Research on sales forecasting of time-sensitive commodities in the literature is mainly at the theoretical level. For most ordinary retailers who do not understand model algorithms, it is difficult to apply operations

[0007] 4. The collected historical data may have different characteristics due to the different effects of weather, holidays and other factors on sales. These characteristics may cause the historical data to follow different laws and cause greater interference to the modeling

[0008] 5. These algorithm models are mainly based on data to debug the model, the data changes, the model needs to be adjusted accordingly, and the generalization performance of the model is not high

[0009] 6. Existing algorithm models only predict estimated values ​​and only point estimated values, without considering the influence of random distribution

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