A predictive production-logistics cooperation method for service-oriented smart discrete manufacturing system
Wenbo Wang, Yunzhu Shan, Yue Xi, Zilin Xia, Gongyue Xu, Xinzhou Zhang
Abstract
Production logistics cooperation plays an important role in the service-oriented manufacturing system. However, most of the existing manufacturing systems use passive mode to assign the production and logistics machines, which often leads to long waiting times for work-in-progresses and unfeasibility of assigned tasks. To address these issues, a predictive production-logistics cooperation method is discussed. Firstly, a cloud service encapsulation method is applied in the discrete manufacturing system to create an intelligent service-sensing environment. Secondly, the deep learning-based key manufacturing service performance prediction model is designed to forecast the future service capacity of the production and logistics machines. Thirdly, a multi-objective production-logistics cooperation decision-making method is proposed to obtain the optimal predictive manufacturing task allocation and corresponding logistics assignment results. Lastly, a case study from a typical discrete manufacturing system is used to demonstrate the presented method. The results show that the proposed method can largely improve production efficiency.