Research on time forecasting model in digital twin for logistic activity in intelligent manufactory

Abstract

In the era of intelligent manufacturing, the production mode of customer demand-pull has become predominant. However, this mode of production entails numerous uncertainties, necessitating accurate predictions in various aspects such as market demand, supply chain, warehousing, and workshop logistics. Therefore, it is of great engineering significance to establish a high-performance time forecasting model for enhancing logistics planning and operations. In this study, we propose a novel hybrid time series forecasting method. This method can select appropriate decomposition methods and prediction models based on the characteristics of the sequence itself, and use hyperparameter optimization to achieve the best prediction effect. The effectiveness of the proposed method is demonstrated through rigorous validation with diverse types of time series data. Consequently, this method holds promise as a suitable forecasting model for logistics planning and operations.