Individual-Based Transfer Learning for Dynamic Human-Robot Collaborative Disassembly Line Balancing

This paper analyzed the human-robot collaborative disassembly line balancing problem, which is significantly different from the traditional disassembly line balancing problem. In a human-robot collaborative disassembly line, multiple people and robots perform disassembly tasks at each workstation. Due to the uncertainties such as product quality and human capabilities, the human-robot collaborative disassembly line balancing problem is a dynamic optimization problem. We take into account the uncertainty of product quality and personnel capabilities. In addition, dynamic optimization problems require fast and accurate tracking of Pareto’s optimal solution set in a changing environment, and transfer learning has been proven appropriate. Therefore, an individual-based transfer learning-assisted evolutionary dynamic optimization algorithm has been developed to handle the human-robot collaborative disassembly line balancing problem. The algorithm uses an individual-based transfer learning technique to reuse experience, which accelerates the generation of the initial population and improves the convergence speed of solutions. Finally, based on a set of problem examples generated in this paper, the proposed algorithm is compared and analyzed with several competitors in terms of the mean inverted generational distance and the mean hyper-volume, verifying the effectiveness of the proposed algorithm on the dynamic human-robot collaborative disassembly line balancing. The results show that the proposed algorithm has good performance in large scale problems.