The Intelligent Decision-making based on Multisource Heterogeneous Data Fusion in Manufacturing

Abstract

In the complex manufacturing environment, the information collected from various information sources often has a certain degree of uncertainty and ambiguity, and even be contradictory which is difficult to support decision-making effectively. In this paper, an efficient intelligent decision-making method based on multi-source heterogeneous data fusion is proposed. Firstly, under the rough set theory, the attribute reduction method based on the improved particle swarm optimization is proposed to efficiently obtain decision-related attributes. Secondly, using the improved Dempster-Shafer (D-S) evidence theory to fuse and calculate the reduced information sources to obtain the final decision results. Finally, a space factory was taken as the application object to verify the feasibility of proposed technology.