Research on Evolution Balancing for Product Family Assembly Line in Big Data Environment

This is the authors' abstract. We don't add key points for this paper.

Aiming at the problem of product family assembly line (PFAL) evolution balancing, an evolution balancing model for PFAL is established and an improved algorithm based on NSGA_II is also proposed. Firstly, the product family evolution and assembly line characteristics are researched and analyzed in big data environment. Tasks on PFAL are divided into platform and personality tasks, and the stability of assembly tasks is mainly considered especially. In the optimization process, a chromosome encoding based on TOP sorting algorithm is adopted, and a new density selection and decoding algorithm is proposed to make up for the deficiencies in traditional algorithms. Finally, an example of PFAL planning is given to verify the effectiveness and feasibility of the improved NSGA_II.