## 7 Conclusions  
This paper studied a new dynamic human-robot collaborative disassembly line balancing problem, D-HRDLB, in which multiple humans and robots can perform multiple disassembly operations collaboratively in any workstation of the disassembly line. Various dynamic factors in the disassembly environment, especially uncertain product quality, can be responded to promptly. Therefore, D-HRDLB is closer to the actual disassembly environment in remanufacturing enterprises than the existing DLBPs. Since the existing disassembly process models cannot describe the human-robot collaborative mode and the time-varying environment, a new task-based dynamic disassembly process model, TD-TAOG, was proposed. Then, a formal definition of the model was illustrated. Compared with existing static or quasi-static disassembly process models such as TAOG, the unique features of TD-TAOG are manifested in two aspects: (i) in the selection of basic elements, TAOG chooses the disassembly operation, while TD-TAOG chooses a more fine-grained disassembly task; and (ii) TAOG can only be used to describe products of definite quality, whereas TD-TAOG can characterise the time-varying properties of tasks affected by uncertain product quality. Furthermore, based on TD-TAOG, we formalised the decision variables, the objective functions, and the constraints in D-HRDLB, and we established a multiobjective optimisation model for D-HRDLB. Compared with existing nondeterministic optimisation models for DLBPs, this model is a dynamic optimisation model that can better resolve various uncertain factors. Since D-HRDLB has been identified as NP-hard, a tracking multiobjective dynamic evolutionary algorithm B-DMOEA based on evolutionary dynamic optimisation and feature-based transfer learning was proposed to track and respond to dynamic factors. We analysed the algorithm framework, the initial population generation assisted by transfer learning, the solution encoding and decoding, and the solution variation. In B-DMOEA, the initial population generation is inspired by Tr-DMOEA. However, B-DMOEA differs from Tr-DMOEA in three aspects: (i) B-DMOEA adopts a research strategy based on MOEAs, which can improve the authenticity of the sample distribution; (ii) unlike Tr-DMOEA, which selects the previous historical environment as the source domain, B-DMOEA selects the environment that is most similar to the current environment in the historical environment as the source domain of knowledge transfer; and (iii) B-DMOEA considers the marginal distribution and the conditional distribution of the sample sets, and it can adjust the weights of these two distributions according to the distribution difference between the domains. Tr-DMOEA only considers the marginal distribution of the sample set. These properties of B-DMOEA can effectively reduce the probability of negative transfer. To investigate the behaviour of B-DMOEA on D-HRDLB, a set of D-HRDLB problem instances containing small-scale and large-scale instances were generated as the benchmark. Fifteen small-scale instances showed relatively small environmental changes in the set, and 24 large-scale instances displayed relatively significant environmental changes. Based on the generated instance set, we selected three representative MOEAs, including NSGA-II, RVEA-i, and IBEA, incorporated them into Tr-DMOEA and B-DMOEA, respectively, and compared the performance of these obtained algorithms through experiments. Experimental results show, first, that in B-DMOEA, the value \(\mu\) has a significant impact on the quality of the initial population. Regarding most small-scale instances, because the environment has not changed much, the influence of the conditional distribution on the effect of transfer learning is more significant. In contrast, considering most large-scale instances, due to significant environmental changes, the marginal distribution has a more significant impact on the effect of transfer learning. Second, B-DMOEA can perform better than Tr-DMOEA.