Research on Game-Theoretic Supply Chain Optimisation Strategies Based on Multiple Linear Regression and BP-PSO Combined Time Series Analysis

Ye Li

doi:10.54097/mpv66q51

Authors

Ye Li

DOI:

https://doi.org/10.54097/mpv66q51

Keywords:

Multiple linear regression, BP neural network, Vegetable marketing optimisation decisions, Time series, Game theory.

Abstract

Vegetable commodities have a short cycle, and supermarkets generally replenish goods based on historical sales and demand for each commodity. Vegetable commodities are different from other commodities with relatively stable supply chain planning and market environment, so the automatic pricing and replenishment decisions of fresh food superstores on commodities are of great significance. This paper focuses on the problem of commodity pricing and replenishment strategy. Firstly, through multiple linear analysis regression analysis, the relationship between total quantity and cost plus pricing, fit a one-dimensional linear fitting equation, and visualise the correlation between sales price and total quantity, secondly, observe the trend of various types of vegetables through matlab time-series analysis, establish BP neural network prediction model, predict the total quantity of sales and total quantity of replenishment in the coming week, and formulate the commodity pricing and replenishment strategy using Linear programming to establish the optimization model, verify the feasibility of the model, the parameter normality test and residual test and visual inspection error, analyse the parameter significance, use BP neural network to test the fitting effect. The results of the study, based on the game theory of supply chain replenishment and pricing strategy to establish a mathematical model.

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