基于群体决策特征的中式自选餐厅菜品销量预测模型

Prediction model of dishes sales in Chinese optional restaurant based on group decision characteristics

从中式自选餐厅的运营流水数据中刻画餐饮消费者群体的膳食特征,并对餐厅菜品销量进行预测。由于中式自选餐厅每日供应的菜品变化巨大,传统的时间序列预测方法难以解决问题,为此提出了一种基于决策理论的预测模型。通过MACBETH方法和期望效用理论将餐厅的菜品转换为统一的效用值,作为就餐群体选择菜品的依据,使用决策权重代表消费者群体的膳食特征,然后基于行为决策理论设计餐饮消费者的决策模型来预测各个菜品的销量。使用了中式自选餐厅的流水数据训练销量预测模型,模型在训练集的交叉熵为0.082,在测试集上的交叉熵为0.086。相比基于神经网络的预测模型,该模型精确度更高,并具有良好的解释性。因此,该模型能够精确预测给定菜品供应下中式自选餐厅的菜品销量,从而支持中式自选餐厅的高效运营。

This paper characterized the dietary characteristics of the catering consumer group from sales data of Chinese optional restaurants and predicted the dish sales of the restaurant.Due to the huge changes in the daily dishes served in Chinese optional restaurants,traditional time series forecasting methods can hardly solve the problem.This paper proposed a forecasting model based on decision theory.It used the MACBETH method and the expected utility theory to transform the restaurant’s dishes into a unified utility value,which served as the basis for the dining group to choose dishes,used the decision weight to represent the dietary characteristics of the consumer group,and then designed a decision model for catering consumers based on the behavioral decision theory.This paper used the running data of Chinese optional restaurants for training.The cross-entropy of the prediction model on the training set is 0.082 and the cross-entropy on the test set is 0.086.Compared with the prediction model based on neural network,the model is more accurate and has good interpretability.Therefore,the model can accurately predict the sales volume of Chinese optional restaurants under a given dish supply,thereby supporting the efficient operation of Chinese optional restaurants.