基于马尔科夫链的蔬菜生鲜商超蔬菜销售预测优化模型

Optimization Model of Vegetable Sales Forecasting in Fresh Supermarket Based on Markov Chain

为了解决蔬菜生鲜商超蔬菜类商品的补货问题,提出一种基于马尔科夫链的蔬菜生鲜商超蔬菜销售预测优化模型,通过对蔬菜类商品销量的预测来寻找最优补货策略。该模型将各个蔬菜类商品的现有状态分为五个等级,通过马尔科夫链状态概率的不断迭代,对未来商超各个蔬菜类商品的销量进行预测。依据不同蔬菜类商品的预测销量,使用统计学原理对蔬菜类商品的预测平均利润分析并使用贪心算法寻找到最优补货策略,充分使用MATLAB、Excel对大量数据进行快速准确的分类和处理,可以为各个生鲜商超具有时效性蔬菜的销量预测、补货策略和利润估计提供参考。

In order to solve the replenishment problem of vegetable commodities,an optimization model of vegetable sales forecasting based on Markov chain was proposed.The optimal replenishment strategy was found by forecasting vegetable sales.The model divides the existing state of each vegetable commodity into five levels,and predicts the future sales volume of each vegetable commodity through the continuous iteration of the state probability of Markov chain.According to the predicted sales volume of different vegetable commodities,statistical principles were used to analyze the predicted average profit of vegetable commodities and greedy algorithm was used to find the optimal replenishment strategy.MATLAB and Excel were fully used to classify and process large amounts of data quickly and accurately.It can provide reference for sales forecasting,replenishment strategy and profit estimation of time-sensitive vegetables in each fresh supermarket.