To fill this gap, 首先，我们提出了一种双流程销量预测

To fill this gap, first of all, we propose a dual process sales forecasting framework, which combines a variety of machine learning and clustering algorithms, and use clickstream data to predict sales of online retailers. Then, based on sales forecast results and further taking into account the forecast is not necessarily accurate, we introduce fuzzy demand, construction of multi-objective fuzzy demand multi-period mixed integer programming model and solved by improved genetic algorithms. It solves. . . problem. Numerical experiments verify the validity of the model, and further analysis of the trade-offs between different objectives.

To fill this gap, first of all, we propose a two-process sales forecasting framework that combines a variety of machine learning and clustering algorithms and uses clickstream data to forecast sales from online retailers. Then, based on the sales forecast results, and further considering that the prediction is not necessarily accurate, we introduce fuzzy requirements, construct a multi-target multi-period hybrid integer planning model under fuzzy requirements, and solve them with improved genetic algorithms. It solves... Problem. Numerical experiments verify the validity of the model and further analyze the trade-offs between the different objectives.

To fill this gap, first of all, we propose a dual process sales forecasting framework, which combines a variety of machine learning and clustering algorithms, and uses click stream data to predict the sales of online retailers. Then, based on the results of sales volume forecast, and further considering that the forecast is not necessarily accurate, we introduce fuzzy demand, build a multi-objective and multi cycle mixed integer programming model under fuzzy demand, and use the improved genetic algorithm to solve it. It solved... Problem. Numerical experiments verify the effectiveness of the model, and further analyze the trade-off between different objectives.