Genetic Transfer Learning for Optimizing and Balancing of Assembly Lines

In this article, the genetic transfer learning (GTL) method is proposed for knowledge transfer of sustainable assembly manufacturing systems. Existing methods for the assembly line balancing problem, such as genetic algorithms (GAs), suffer from three significant limitations, i.e., tedious “trial-and-error” processes, no utilization of existing system solutions, and no consideration of new constraints on system reconfiguration. To address these problems, we propose GTL to migrate the knowledge of the GA setup in system reconfiguration. The contributions of this article are as follows. First, transfer pretreatment is performed to formulate knowledge of existing systems and adapt to the new constraints of future systems. Second, the similarity of existing and future systems is defined quantifiably to determine transfer conditions and avoid weak and negative transfer for maximizing knowledge transfer. Finally, the transfer strategy is made to determine the method and knowledge to be transferred. The case study of a computer assembly line shows that adopting transfer learning has helped to improve assembly line efficiency and sustainability.